Overview

Dataset statistics

Number of variables81
Number of observations1452
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory962.5 KiB
Average record size in memory678.8 B

Variable types

Numeric29
Categorical50
Boolean1
Unsupported1

Alerts

MSSubClass is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
OverallQual is highly overall correlated with YearBuilt and 6 other fieldsHigh correlation
YearBuilt is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly overall correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with MSSubClass and 3 other fieldsHigh correlation
GrLivArea is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
GarageArea is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
PoolArea is highly overall correlated with PoolQCHigh correlation
MiscVal is highly overall correlated with MiscFeatureHigh correlation
SalePrice is highly overall correlated with OverallQual and 7 other fieldsHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
Neighborhood is highly overall correlated with MSZoningHigh correlation
BldgType is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
ExterQual is highly overall correlated with OverallQual and 1 other fieldsHigh correlation
Foundation is highly overall correlated with YearBuilt and 1 other fieldsHigh correlation
BsmtQual is highly overall correlated with Foundation and 3 other fieldsHigh correlation
BsmtCond is highly overall correlated with BsmtQual and 1 other fieldsHigh correlation
BsmtExposure is highly overall correlated with BsmtQual and 1 other fieldsHigh correlation
BsmtFinType1 is highly overall correlated with BsmtQual and 2 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
KitchenQual is highly overall correlated with OverallQual and 1 other fieldsHigh correlation
Fireplaces is highly overall correlated with FireplaceQuHigh correlation
FireplaceQu is highly overall correlated with FireplacesHigh correlation
GarageType is highly overall correlated with GarageFinish and 1 other fieldsHigh correlation
GarageFinish is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageQual is highly overall correlated with GarageFinish and 2 other fieldsHigh correlation
GarageCond is highly overall correlated with GarageFinish and 2 other fieldsHigh correlation
PoolQC is highly overall correlated with PoolAreaHigh correlation
MiscFeature is highly overall correlated with MiscValHigh correlation
MSZoning is highly imbalanced (57.1%)Imbalance
Street is highly imbalanced (96.1%)Imbalance
Alley is highly imbalanced (75.0%)Imbalance
LandContour is highly imbalanced (68.2%)Imbalance
Utilities is highly imbalanced (99.2%)Imbalance
LandSlope is highly imbalanced (78.7%)Imbalance
Condition1 is highly imbalanced (71.6%)Imbalance
Condition2 is highly imbalanced (96.4%)Imbalance
BldgType is highly imbalanced (59.3%)Imbalance
RoofStyle is highly imbalanced (65.0%)Imbalance
RoofMatl is highly imbalanced (94.4%)Imbalance
ExterCond is highly imbalanced (72.7%)Imbalance
BsmtCond is highly imbalanced (72.4%)Imbalance
BsmtFinType2 is highly imbalanced (66.9%)Imbalance
Heating is highly imbalanced (92.6%)Imbalance
CentralAir is highly imbalanced (65.1%)Imbalance
Electrical is highly imbalanced (80.1%)Imbalance
BsmtHalfBath is highly imbalanced (79.6%)Imbalance
KitchenAbvGr is highly imbalanced (85.8%)Imbalance
Functional is highly imbalanced (82.0%)Imbalance
GarageQual is highly imbalanced (75.4%)Imbalance
GarageCond is highly imbalanced (77.4%)Imbalance
PavedDrive is highly imbalanced (69.8%)Imbalance
PoolQC is highly imbalanced (97.4%)Imbalance
Fence is highly imbalanced (56.3%)Imbalance
MiscFeature is highly imbalanced (89.2%)Imbalance
SaleType is highly imbalanced (75.4%)Imbalance
SaleCondition is highly imbalanced (62.8%)Imbalance
MiscVal is highly skewed (γ1 = 24.40988876)Skewed
Id is uniformly distributedUniform
Id has unique valuesUnique
GarageYrBlt is an unsupported type, check if it needs cleaning or further analysisUnsupported
MasVnrArea has 861 (59.3%) zerosZeros
BsmtFinSF1 has 465 (32.0%) zerosZeros
BsmtFinSF2 has 1285 (88.5%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 824 (56.7%) zerosZeros
LowQualFinSF has 1426 (98.2%) zerosZeros
GarageArea has 81 (5.6%) zerosZeros
WoodDeckSF has 755 (52.0%) zerosZeros
OpenPorchSF has 654 (45.0%) zerosZeros
EnclosedPorch has 1245 (85.7%) zerosZeros
3SsnPorch has 1428 (98.3%) zerosZeros
ScreenPorch has 1336 (92.0%) zerosZeros
PoolArea has 1445 (99.5%) zerosZeros
MiscVal has 1400 (96.4%) zerosZeros

Reproduction

Analysis started2023-01-10 03:34:04.101592
Analysis finished2023-01-10 03:43:05.107835
Duration9 minutes and 1.01 second
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1452
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729.82231
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:05.492852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.55
Q1364.75
median729.5
Q31095.25
95-th percentile1387.45
Maximum1460
Range1459
Interquartile range (IQR)730.5

Descriptive statistics

Standard deviation421.93812
Coefficient of variation (CV)0.57813815
Kurtosis-1.2007192
Mean729.82231
Median Absolute Deviation (MAD)365.5
Skewness0.0029085294
Sum1059702
Variance178031.78
MonotonicityStrictly increasing
2023-01-10T04:43:06.138270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
982 1
 
0.1%
980 1
 
0.1%
979 1
 
0.1%
977 1
 
0.1%
976 1
 
0.1%
975 1
 
0.1%
973 1
 
0.1%
972 1
 
0.1%
971 1
 
0.1%
Other values (1442) 1442
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1460 1
0.1%
1459 1
0.1%
1458 1
0.1%
1457 1
0.1%
1456 1
0.1%
1455 1
0.1%
1454 1
0.1%
1453 1
0.1%
1452 1
0.1%
1451 1
0.1%

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.949036
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:06.723757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.340097
Coefficient of variation (CV)0.74347347
Kurtosis1.5764254
Mean56.949036
Median Absolute Deviation (MAD)30
Skewness1.4073359
Sum82690
Variance1792.6838
MonotonicityNot monotonic
2023-01-10T04:43:07.172387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 532
36.6%
60 296
20.4%
50 144
 
9.9%
120 86
 
5.9%
30 69
 
4.8%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.3%
ValueCountFrequency (%)
20 532
36.6%
30 69
 
4.8%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 296
20.4%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 86
 
5.9%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 296
20.4%

MSZoning
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
RL
1146 
RM
218 
FV
 
62
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0344353
Min length2

Characters and Unicode

Total characters2954
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1146
78.9%
RM 218
 
15.0%
FV 62
 
4.3%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2023-01-10T04:43:07.716451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:08.301823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rl 1146
78.4%
rm 218
 
14.9%
fv 62
 
4.2%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1380
46.7%
L 1146
38.8%
M 218
 
7.4%
F 62
 
2.1%
V 62
 
2.1%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2894
98.0%
Lowercase Letter 30
 
1.0%
Space Separator 10
 
0.3%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1380
47.7%
L 1146
39.6%
M 218
 
7.5%
F 62
 
2.1%
V 62
 
2.1%
H 16
 
0.6%
C 10
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 20
66.7%
a 10
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2924
99.0%
Common 30
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1380
47.2%
L 1146
39.2%
M 218
 
7.5%
F 62
 
2.1%
V 62
 
2.1%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
a 10
 
0.3%
Common
ValueCountFrequency (%)
10
33.3%
( 10
33.3%
) 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1380
46.7%
L 1146
38.8%
M 218
 
7.4%
F 62
 
2.1%
V 62
 
2.1%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotFrontage
Real number (ℝ)

Distinct343
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.606375
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:08.880299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35
Q160
median70
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.950455
Coefficient of variation (CV)0.35337397
Kurtosis20.009349
Mean70.606375
Median Absolute Deviation (MAD)10
Skewness2.6563633
Sum102520.46
Variance622.52522
MonotonicityNot monotonic
2023-01-10T04:43:09.483624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.8%
50 57
 
3.9%
75 52
 
3.6%
65 43
 
3.0%
85 40
 
2.8%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (333) 907
62.5%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
30.42813815 1
 
0.1%
31.51100352 3
 
0.2%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 8
 
0.6%
35.26197792 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
277.9046802 1
0.1%
273.0865856 1
0.1%
232.3977348 1
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
163.7946905 1
0.1%
160 1
0.1%
158.0038975 1
0.1%

LotArea
Real number (ℝ)

Distinct1067
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10507.276
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:10.149411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3277.3
Q17538.75
median9478.5
Q311600
95-th percentile17299.35
Maximum215245
Range213945
Interquartile range (IQR)4061.25

Descriptive statistics

Standard deviation9989.5636
Coefficient of variation (CV)0.95072818
Kurtosis203.72688
Mean10507.276
Median Absolute Deviation (MAD)1998
Skewness12.240033
Sum15256565
Variance99791381
MonotonicityNot monotonic
2023-01-10T04:43:10.777387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.7%
6000 17
 
1.2%
8400 14
 
1.0%
9000 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
6120 8
 
0.6%
6240 8
 
0.6%
Other values (1057) 1309
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Street
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Pave
1446 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5808
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1446
99.6%
Grvl 6
 
0.4%

Length

2023-01-10T04:43:11.339199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:11.860611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pave 1446
99.6%
grvl 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 1452
25.0%
P 1446
24.9%
a 1446
24.9%
e 1446
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4356
75.0%
Uppercase Letter 1452
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1452
33.3%
a 1446
33.2%
e 1446
33.2%
r 6
 
0.1%
l 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 1446
99.6%
G 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5808
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 1452
25.0%
P 1446
24.9%
a 1446
24.9%
e 1446
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 1452
25.0%
P 1446
24.9%
a 1446
24.9%
e 1446
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Alley
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoInfo
1362 
Grvl
 
50
Pave
 
40

Length

Max length6
Median length6
Mean length5.8760331
Min length4

Characters and Unicode

Total characters8532
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoInfo
2nd rowNoInfo
3rd rowNoInfo
4th rowNoInfo
5th rowNoInfo

Common Values

ValueCountFrequency (%)
NoInfo 1362
93.8%
Grvl 50
 
3.4%
Pave 40
 
2.8%

Length

2023-01-10T04:43:12.334553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:12.937233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 1362
93.8%
grvl 50
 
3.4%
pave 40
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 2724
31.9%
N 1362
16.0%
I 1362
16.0%
n 1362
16.0%
f 1362
16.0%
v 90
 
1.1%
G 50
 
0.6%
r 50
 
0.6%
l 50
 
0.6%
P 40
 
0.5%
Other values (2) 80
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5718
67.0%
Uppercase Letter 2814
33.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2724
47.6%
n 1362
23.8%
f 1362
23.8%
v 90
 
1.6%
r 50
 
0.9%
l 50
 
0.9%
a 40
 
0.7%
e 40
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N 1362
48.4%
I 1362
48.4%
G 50
 
1.8%
P 40
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8532
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2724
31.9%
N 1362
16.0%
I 1362
16.0%
n 1362
16.0%
f 1362
16.0%
v 90
 
1.1%
G 50
 
0.6%
r 50
 
0.6%
l 50
 
0.6%
P 40
 
0.5%
Other values (2) 80
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2724
31.9%
N 1362
16.0%
I 1362
16.0%
n 1362
16.0%
f 1362
16.0%
v 90
 
1.1%
G 50
 
0.6%
r 50
 
0.6%
l 50
 
0.6%
P 40
 
0.5%
Other values (2) 80
 
0.9%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Reg
919 
IR1
482 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4356
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 919
63.3%
IR1 482
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2023-01-10T04:43:13.383922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:13.937019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
reg 919
63.3%
ir1 482
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1452
33.3%
e 919
21.1%
g 919
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1985
45.6%
Lowercase Letter 1838
42.2%
Decimal Number 533
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 482
90.4%
2 41
 
7.7%
3 10
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
R 1452
73.1%
I 533
 
26.9%
Lowercase Letter
ValueCountFrequency (%)
e 919
50.0%
g 919
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3823
87.8%
Common 533
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1452
38.0%
e 919
24.0%
g 919
24.0%
I 533
 
13.9%
Common
ValueCountFrequency (%)
1 482
90.4%
2 41
 
7.7%
3 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1452
33.3%
e 919
21.1%
g 919
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

LandContour
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Lvl
1303 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4356
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1303
89.7%
Bnk 63
 
4.3%
HLS 50
 
3.4%
Low 36
 
2.5%

Length

2023-01-10T04:43:14.408463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:14.990426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1303
89.7%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 1389
31.9%
v 1303
29.9%
l 1303
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2804
64.4%
Uppercase Letter 1552
35.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1303
46.5%
l 1303
46.5%
n 63
 
2.2%
k 63
 
2.2%
o 36
 
1.3%
w 36
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 1389
89.5%
B 63
 
4.1%
H 50
 
3.2%
S 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1389
31.9%
v 1303
29.9%
l 1303
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1389
31.9%
v 1303
29.9%
l 1303
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utilities
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
AllPub
1451 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8712
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1451
99.9%
NoSeWa 1
 
0.1%

Length

2023-01-10T04:43:15.450357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:15.969287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1451
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2902
33.3%
A 1451
16.7%
P 1451
16.7%
u 1451
16.7%
b 1451
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5807
66.7%
Uppercase Letter 2905
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2902
50.0%
u 1451
25.0%
b 1451
25.0%
o 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 1451
49.9%
P 1451
49.9%
N 1
 
< 0.1%
S 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8712
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2902
33.3%
A 1451
16.7%
P 1451
16.7%
u 1451
16.7%
b 1451
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2902
33.3%
A 1451
16.7%
P 1451
16.7%
u 1451
16.7%
b 1451
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Inside
1046 
Corner
262 
CulDSac
 
93
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.9586777
Min length3

Characters and Unicode

Total characters8652
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside 1046
72.0%
Corner 262
 
18.0%
CulDSac 93
 
6.4%
FR2 47
 
3.2%
FR3 4
 
0.3%

Length

2023-01-10T04:43:16.444426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:17.094025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
inside 1046
72.0%
corner 262
 
18.0%
culdsac 93
 
6.4%
fr2 47
 
3.2%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1308
15.1%
n 1308
15.1%
I 1046
12.1%
s 1046
12.1%
i 1046
12.1%
d 1046
12.1%
r 524
6.1%
C 355
 
4.1%
o 262
 
3.0%
S 93
 
1.1%
Other values (9) 618
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6912
79.9%
Uppercase Letter 1689
 
19.5%
Decimal Number 51
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1308
18.9%
n 1308
18.9%
s 1046
15.1%
i 1046
15.1%
d 1046
15.1%
r 524
7.6%
o 262
 
3.8%
c 93
 
1.3%
a 93
 
1.3%
u 93
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
I 1046
61.9%
C 355
 
21.0%
S 93
 
5.5%
D 93
 
5.5%
F 51
 
3.0%
R 51
 
3.0%
Decimal Number
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 8601
99.4%
Common 51
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1308
15.2%
n 1308
15.2%
I 1046
12.2%
s 1046
12.2%
i 1046
12.2%
d 1046
12.2%
r 524
6.1%
C 355
 
4.1%
o 262
 
3.0%
S 93
 
1.1%
Other values (7) 567
6.6%
Common
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1308
15.1%
n 1308
15.1%
I 1046
12.1%
s 1046
12.1%
i 1046
12.1%
d 1046
12.1%
r 524
6.1%
C 355
 
4.1%
o 262
 
3.0%
S 93
 
1.1%
Other values (9) 618
7.1%

LandSlope
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Gtl
1374 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4356
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1374
94.6%
Mod 65
 
4.5%
Sev 13
 
0.9%

Length

2023-01-10T04:43:17.587691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:18.119482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1374
94.6%
mod 65
 
4.5%
sev 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G 1374
31.5%
t 1374
31.5%
l 1374
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2904
66.7%
Uppercase Letter 1452
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1374
47.3%
l 1374
47.3%
o 65
 
2.2%
d 65
 
2.2%
e 13
 
0.4%
v 13
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
G 1374
94.6%
M 65
 
4.5%
S 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 1374
31.5%
t 1374
31.5%
l 1374
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 1374
31.5%
t 1374
31.5%
l 1374
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Neighborhood
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NAmes
225 
CollgCr
149 
OldTown
113 
Edwards
100 
Somerst
83 
Other values (20)
782 

Length

Max length7
Median length7
Mean length6.4917355
Min length5

Characters and Unicode

Total characters9426
Distinct characters38
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 225
15.5%
CollgCr 149
 
10.3%
OldTown 113
 
7.8%
Edwards 100
 
6.9%
Somerst 83
 
5.7%
Gilbert 78
 
5.4%
NridgHt 76
 
5.2%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%
SawyerW 58
 
4.0%
Other values (15) 423
29.1%

Length

2023-01-10T04:43:18.598826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 225
15.5%
collgcr 149
 
10.3%
oldtown 113
 
7.8%
edwards 100
 
6.9%
somerst 83
 
5.7%
gilbert 78
 
5.4%
nridght 76
 
5.2%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 58
 
4.0%
Other values (15) 423
29.1%

Most occurring characters

ValueCountFrequency (%)
r 922
 
9.8%
e 900
 
9.5%
l 619
 
6.6%
d 505
 
5.4%
s 483
 
5.1%
o 478
 
5.1%
m 436
 
4.6%
N 424
 
4.5%
w 412
 
4.4%
C 404
 
4.3%
Other values (28) 3843
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6719
71.3%
Uppercase Letter 2707
28.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 922
13.7%
e 900
13.4%
l 619
9.2%
d 505
 
7.5%
s 483
 
7.2%
o 478
 
7.1%
m 436
 
6.5%
w 412
 
6.1%
i 349
 
5.2%
a 343
 
5.1%
Other values (10) 1272
18.9%
Uppercase Letter
ValueCountFrequency (%)
N 424
15.7%
C 404
14.9%
S 348
12.9%
A 298
11.0%
T 188
6.9%
W 156
 
5.8%
O 150
 
5.5%
B 118
 
4.4%
R 115
 
4.2%
E 100
 
3.7%
Other values (8) 406
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9426
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 922
 
9.8%
e 900
 
9.5%
l 619
 
6.6%
d 505
 
5.4%
s 483
 
5.1%
o 478
 
5.1%
m 436
 
4.6%
N 424
 
4.5%
w 412
 
4.4%
C 404
 
4.3%
Other values (28) 3843
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 922
 
9.8%
e 900
 
9.5%
l 619
 
6.6%
d 505
 
5.4%
s 483
 
5.1%
o 478
 
5.1%
m 436
 
4.6%
N 424
 
4.5%
w 412
 
4.4%
C 404
 
4.3%
Other values (28) 3843
40.8%

Condition1
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Norm
1252 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.1219008
Min length4

Characters and Unicode

Total characters5985
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1252
86.2%
Feedr 81
 
5.6%
Artery 48
 
3.3%
RRAn 26
 
1.8%
PosN 19
 
1.3%
RRAe 11
 
0.8%
PosA 8
 
0.6%
RRNn 5
 
0.3%
RRNe 2
 
0.1%

Length

2023-01-10T04:43:19.178583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:19.873345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
norm 1252
86.2%
feedr 81
 
5.6%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 8
 
0.6%
rrnn 5
 
0.3%
rrne 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1429
23.9%
o 1279
21.4%
N 1278
21.4%
m 1252
20.9%
e 223
 
3.7%
A 93
 
1.6%
R 88
 
1.5%
F 81
 
1.4%
d 81
 
1.4%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4418
73.8%
Uppercase Letter 1567
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1429
32.3%
o 1279
28.9%
m 1252
28.3%
e 223
 
5.0%
d 81
 
1.8%
t 48
 
1.1%
y 48
 
1.1%
n 31
 
0.7%
s 27
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 1278
81.6%
A 93
 
5.9%
R 88
 
5.6%
F 81
 
5.2%
P 27
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 5985
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1429
23.9%
o 1279
21.4%
N 1278
21.4%
m 1252
20.9%
e 223
 
3.7%
A 93
 
1.6%
R 88
 
1.5%
F 81
 
1.4%
d 81
 
1.4%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1429
23.9%
o 1279
21.4%
N 1278
21.4%
m 1252
20.9%
e 223
 
3.7%
A 93
 
1.6%
R 88
 
1.5%
F 81
 
1.4%
d 81
 
1.4%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Condition2
Categorical

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Norm
1437 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.0068871
Min length4

Characters and Unicode

Total characters5818
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1437
99.0%
Feedr 6
 
0.4%
Artery 2
 
0.1%
RRNn 2
 
0.1%
PosN 2
 
0.1%
PosA 1
 
0.1%
RRAn 1
 
0.1%
RRAe 1
 
0.1%

Length

2023-01-10T04:43:20.476135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:21.163118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
norm 1437
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1447
24.9%
N 1441
24.8%
o 1440
24.8%
m 1437
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4355
74.9%
Uppercase Letter 1463
 
25.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1447
33.2%
o 1440
33.1%
m 1437
33.0%
e 15
 
0.3%
d 6
 
0.1%
n 3
 
0.1%
s 3
 
0.1%
t 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1441
98.5%
R 8
 
0.5%
F 6
 
0.4%
A 5
 
0.3%
P 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5818
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1447
24.9%
N 1441
24.8%
o 1440
24.8%
m 1437
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1447
24.9%
N 1441
24.8%
o 1440
24.8%
m 1437
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
1Fam
1213 
TwnhsE
 
113
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2995868
Min length4

Characters and Unicode

Total characters6243
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1213
83.5%
TwnhsE 113
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
3.0%
2fmCon 31
 
2.1%

Length

2023-01-10T04:43:21.743389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:22.370827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1213
83.5%
twnhse 113
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
3.0%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1244
19.9%
1 1213
19.4%
a 1213
19.4%
F 1213
19.4%
n 187
 
3.0%
T 156
 
2.5%
w 156
 
2.5%
h 156
 
2.5%
s 156
 
2.5%
E 113
 
1.8%
Other values (10) 436
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3434
55.0%
Uppercase Letter 1565
25.1%
Decimal Number 1244
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1244
36.2%
a 1213
35.3%
n 187
 
5.4%
w 156
 
4.5%
h 156
 
4.5%
s 156
 
4.5%
l 52
 
1.5%
x 52
 
1.5%
e 52
 
1.5%
p 52
 
1.5%
Other values (3) 114
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
F 1213
77.5%
T 156
 
10.0%
E 113
 
7.2%
D 52
 
3.3%
C 31
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1213
97.5%
2 31
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4999
80.1%
Common 1244
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1244
24.9%
a 1213
24.3%
F 1213
24.3%
n 187
 
3.7%
T 156
 
3.1%
w 156
 
3.1%
h 156
 
3.1%
s 156
 
3.1%
E 113
 
2.3%
l 52
 
1.0%
Other values (8) 353
 
7.1%
Common
ValueCountFrequency (%)
1 1213
97.5%
2 31
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1244
19.9%
1 1213
19.4%
a 1213
19.4%
F 1213
19.4%
n 187
 
3.0%
T 156
 
2.5%
w 156
 
2.5%
h 156
 
2.5%
s 156
 
2.5%
E 113
 
1.8%
Other values (10) 436
 
7.0%

HouseStyle
Categorical

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
1Story
721 
2Story
442 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9104683
Min length4

Characters and Unicode

Total characters8582
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 721
49.7%
2Story 442
30.4%
1.5Fin 154
 
10.6%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.6%

Length

2023-01-10T04:43:23.702161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:24.390321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1story 721
49.7%
2story 442
30.4%
1.5fin 154
 
10.6%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 1265
14.7%
o 1200
14.0%
r 1200
14.0%
y 1200
14.0%
t 1163
13.6%
1 889
10.4%
2 461
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5304
61.8%
Uppercase Letter 1554
 
18.1%
Decimal Number 1537
 
17.9%
Other Punctuation 187
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1200
22.6%
r 1200
22.6%
y 1200
22.6%
t 1163
21.9%
n 187
 
3.5%
i 162
 
3.1%
v 65
 
1.2%
l 65
 
1.2%
e 37
 
0.7%
f 25
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1265
81.4%
F 199
 
12.8%
L 65
 
4.2%
U 25
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 889
57.8%
2 461
30.0%
5 187
 
12.2%
Other Punctuation
ValueCountFrequency (%)
. 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6858
79.9%
Common 1724
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1265
18.4%
o 1200
17.5%
r 1200
17.5%
y 1200
17.5%
t 1163
17.0%
F 199
 
2.9%
n 187
 
2.7%
i 162
 
2.4%
L 65
 
0.9%
v 65
 
0.9%
Other values (4) 152
 
2.2%
Common
ValueCountFrequency (%)
1 889
51.6%
2 461
26.7%
5 187
 
10.8%
. 187
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1265
14.7%
o 1200
14.0%
r 1200
14.0%
y 1200
14.0%
t 1163
13.6%
1 889
10.4%
2 461
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.4%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0929752
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:24.928987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3812889
Coefficient of variation (CV)0.22670187
Kurtosis0.085723433
Mean6.0929752
Median Absolute Deviation (MAD)1
Skewness0.21463562
Sum8847
Variance1.9079589
MonotonicityNot monotonic
2023-01-10T04:43:25.342312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.3%
6 372
25.6%
7 315
21.7%
8 167
11.5%
4 116
 
8.0%
9 43
 
3.0%
3 20
 
1.4%
10 17
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
8.0%
5 397
27.3%
6 372
25.6%
7 315
21.7%
8 167
11.5%
9 43
 
3.0%
10 17
 
1.2%
ValueCountFrequency (%)
10 17
 
1.2%
9 43
 
3.0%
8 167
11.5%
7 315
21.7%
6 372
25.6%
5 397
27.3%
4 116
 
8.0%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5792011
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:25.777308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1131356
Coefficient of variation (CV)0.19951523
Kurtosis1.0942791
Mean5.5792011
Median Absolute Deviation (MAD)0
Skewness0.69492878
Sum8101
Variance1.2390709
MonotonicityNot monotonic
2023-01-10T04:43:26.209559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 815
56.1%
6 251
 
17.3%
7 205
 
14.1%
8 72
 
5.0%
4 57
 
3.9%
3 24
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 24
 
1.7%
4 57
 
3.9%
5 815
56.1%
6 251
 
17.3%
7 205
 
14.1%
8 72
 
5.0%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
5.0%
7 205
 
14.1%
6 251
 
17.3%
5 815
56.1%
4 57
 
3.9%
3 24
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.1164
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:26.830961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1972
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.193761
Coefficient of variation (CV)0.015318102
Kurtosis-0.44215124
Mean1971.1164
Median Absolute Deviation (MAD)25
Skewness-0.60891547
Sum2862061
Variance911.66322
MonotonicityNot monotonic
2023-01-10T04:43:27.463088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 65
 
4.5%
2005 64
 
4.4%
2004 54
 
3.7%
2007 47
 
3.2%
2003 44
 
3.0%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1032
71.1%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 47
3.2%
2006 65
4.5%
2005 64
4.4%
2004 54
3.7%
2003 44
3.0%
2002 21
 
1.4%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.7755
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:28.125959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11966
median1993
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)38

Descriptive statistics

Standard deviation20.652466
Coefficient of variation (CV)0.010405442
Kurtosis-1.2786736
Mean1984.7755
Median Absolute Deviation (MAD)13
Skewness-0.49728105
Sum2881894
Variance426.52433
MonotonicityNot monotonic
2023-01-10T04:43:28.773194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.3%
2006 96
 
6.6%
2007 74
 
5.1%
2005 73
 
5.0%
2004 62
 
4.3%
2000 55
 
3.8%
2003 50
 
3.4%
2002 46
 
3.2%
2008 39
 
2.7%
1996 36
 
2.5%
Other values (51) 743
51.2%
ValueCountFrequency (%)
1950 178
12.3%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 39
2.7%
2007 74
5.1%
2006 96
6.6%
2005 73
5.0%
2004 62
4.3%
2003 50
3.4%
2002 46
3.2%
2001 21
 
1.4%

RoofStyle
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Gable
1134 
Hip
285 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6219008
Min length3

Characters and Unicode

Total characters6711
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1134
78.1%
Hip 285
 
19.6%
Flat 13
 
0.9%
Gambrel 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2023-01-10T04:43:29.386187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:30.035111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
gable 1134
78.1%
hip 285
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1172
17.5%
l 1158
17.3%
e 1147
17.1%
G 1145
17.1%
b 1145
17.1%
H 285
 
4.2%
i 285
 
4.2%
p 285
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5259
78.4%
Uppercase Letter 1452
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1172
22.3%
l 1158
22.0%
e 1147
21.8%
b 1145
21.8%
i 285
 
5.4%
p 285
 
5.4%
r 18
 
0.3%
t 13
 
0.2%
m 11
 
0.2%
d 9
 
0.2%
Other values (3) 16
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G 1145
78.9%
H 285
 
19.6%
F 13
 
0.9%
M 7
 
0.5%
S 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6711
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1172
17.5%
l 1158
17.3%
e 1147
17.1%
G 1145
17.1%
b 1145
17.1%
H 285
 
4.2%
i 285
 
4.2%
p 285
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1172
17.5%
l 1158
17.3%
e 1147
17.1%
G 1145
17.1%
b 1145
17.1%
H 285
 
4.2%
i 285
 
4.2%
p 285
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

RoofMatl
Categorical

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
CompShg
1426 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.9965565
Min length4

Characters and Unicode

Total characters10159
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1426
98.2%
Tar&Grv 11
 
0.8%
WdShngl 6
 
0.4%
WdShake 5
 
0.3%
Metal 1
 
0.1%
Membran 1
 
0.1%
Roll 1
 
0.1%
ClyTile 1
 
0.1%

Length

2023-01-10T04:43:30.573115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:31.230723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1426
98.2%
tar&grv 11
 
0.8%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%
clytile 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1437
14.1%
h 1437
14.1%
g 1432
14.1%
C 1427
14.0%
m 1427
14.0%
o 1427
14.0%
p 1426
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7247
71.3%
Uppercase Letter 2901
28.6%
Other Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 1437
19.8%
g 1432
19.8%
m 1427
19.7%
o 1427
19.7%
p 1426
19.7%
r 23
 
0.3%
a 18
 
0.2%
l 11
 
0.2%
d 11
 
0.2%
v 11
 
0.2%
Other values (7) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 1437
49.5%
C 1427
49.2%
T 12
 
0.4%
W 11
 
0.4%
G 11
 
0.4%
M 2
 
0.1%
R 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
& 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10148
99.9%
Common 11
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1437
14.2%
h 1437
14.2%
g 1432
14.1%
C 1427
14.1%
m 1427
14.1%
o 1427
14.1%
p 1426
14.1%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (14) 82
 
0.8%
Common
ValueCountFrequency (%)
& 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1437
14.1%
h 1437
14.1%
g 1432
14.1%
C 1427
14.0%
m 1427
14.0%
o 1427
14.0%
p 1426
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Exterior1st
Categorical

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
VinylSd
510 
HdBoard
222 
MetalSd
220 
Wd Sdng
205 
Plywood
108 
Other values (10)
187 

Length

Max length7
Median length7
Mean length6.9793388
Min length5

Characters and Unicode

Total characters10134
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 510
35.1%
HdBoard 222
15.3%
MetalSd 220
15.2%
Wd Sdng 205
14.1%
Plywood 108
 
7.4%
CemntBd 59
 
4.1%
BrkFace 50
 
3.4%
WdShing 26
 
1.8%
Stucco 25
 
1.7%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2023-01-10T04:43:31.827849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 510
30.8%
hdboard 222
13.4%
metalsd 220
13.3%
wd 205
12.4%
sdng 205
12.4%
plywood 108
 
6.5%
cemntbd 59
 
3.6%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1777
17.5%
S 1010
 
10.0%
l 839
 
8.3%
n 823
 
8.1%
y 618
 
6.1%
i 536
 
5.3%
V 510
 
5.0%
a 492
 
4.9%
o 468
 
4.6%
B 334
 
3.3%
Other values (22) 2727
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7160
70.7%
Uppercase Letter 2769
 
27.3%
Space Separator 205
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1777
24.8%
l 839
11.7%
n 823
11.5%
y 618
 
8.6%
i 536
 
7.5%
a 492
 
6.9%
o 468
 
6.5%
e 331
 
4.6%
t 307
 
4.3%
r 274
 
3.8%
Other values (10) 695
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
S 1010
36.5%
V 510
18.4%
B 334
 
12.1%
W 231
 
8.3%
H 222
 
8.0%
M 220
 
7.9%
P 108
 
3.9%
C 62
 
2.2%
F 50
 
1.8%
A 21
 
0.8%
Space Separator
ValueCountFrequency (%)
205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9929
98.0%
Common 205
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1777
17.9%
S 1010
10.2%
l 839
 
8.4%
n 823
 
8.3%
y 618
 
6.2%
i 536
 
5.4%
V 510
 
5.1%
a 492
 
5.0%
o 468
 
4.7%
B 334
 
3.4%
Other values (21) 2522
25.4%
Common
ValueCountFrequency (%)
205
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1777
17.5%
S 1010
 
10.0%
l 839
 
8.3%
n 823
 
8.1%
y 618
 
6.1%
i 536
 
5.3%
V 510
 
5.0%
a 492
 
4.9%
o 468
 
4.6%
B 334
 
3.3%
Other values (22) 2727
26.9%

Exterior2nd
Categorical

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
VinylSd
499 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
193 

Length

Max length7
Median length7
Mean length6.9745179
Min length5

Characters and Unicode

Total characters10127
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 499
34.4%
MetalSd 214
14.7%
HdBoard 207
14.3%
Wd Sdng 197
 
13.6%
Plywood 142
 
9.8%
CmentBd 58
 
4.0%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
BrkFace 25
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 26
 
1.8%

Length

2023-01-10T04:43:32.431516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 499
29.5%
wd 235
13.9%
metalsd 214
12.6%
hdboard 207
12.2%
sdng 197
 
11.6%
plywood 142
 
8.4%
cmentbd 58
 
3.4%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 53
 
3.1%

Most occurring characters

ValueCountFrequency (%)
d 1759
17.4%
S 1011
 
10.0%
l 856
 
8.5%
n 826
 
8.2%
y 641
 
6.3%
o 522
 
5.2%
V 499
 
4.9%
i 499
 
4.9%
a 446
 
4.4%
t 313
 
3.1%
Other values (23) 2755
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7154
70.6%
Uppercase Letter 2731
 
27.0%
Space Separator 242
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1759
24.6%
l 856
12.0%
n 826
11.5%
y 641
 
9.0%
o 522
 
7.3%
i 499
 
7.0%
a 446
 
6.2%
t 313
 
4.4%
e 302
 
4.2%
g 255
 
3.6%
Other values (10) 735
10.3%
Uppercase Letter
ValueCountFrequency (%)
S 1011
37.0%
V 499
18.3%
B 298
 
10.9%
W 235
 
8.6%
M 214
 
7.8%
H 207
 
7.6%
P 142
 
5.2%
C 66
 
2.4%
F 25
 
0.9%
A 23
 
0.8%
Other values (2) 11
 
0.4%
Space Separator
ValueCountFrequency (%)
242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9885
97.6%
Common 242
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1759
17.8%
S 1011
10.2%
l 856
 
8.7%
n 826
 
8.4%
y 641
 
6.5%
o 522
 
5.3%
V 499
 
5.0%
i 499
 
5.0%
a 446
 
4.5%
t 313
 
3.2%
Other values (22) 2513
25.4%
Common
ValueCountFrequency (%)
242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1759
17.4%
S 1011
 
10.0%
l 856
 
8.5%
n 826
 
8.2%
y 641
 
6.3%
o 522
 
5.2%
V 499
 
4.9%
i 499
 
4.9%
a 446
 
4.4%
t 313
 
3.1%
Other values (23) 2755
27.2%

MasVnrType
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
None
864 
BrkFace
445 
Stone
128 
BrkCmn
 
15

Length

Max length7
Median length4
Mean length5.0282369
Min length4

Characters and Unicode

Total characters7301
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNone
3rd rowBrkFace
4th rowNone
5th rowBrkFace

Common Values

ValueCountFrequency (%)
None 864
59.5%
BrkFace 445
30.6%
Stone 128
 
8.8%
BrkCmn 15
 
1.0%

Length

2023-01-10T04:43:33.009775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:33.587737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
none 864
59.5%
brkface 445
30.6%
stone 128
 
8.8%
brkcmn 15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5389
73.8%
Uppercase Letter 1912
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1437
26.7%
n 1007
18.7%
o 992
18.4%
r 460
 
8.5%
k 460
 
8.5%
a 445
 
8.3%
c 445
 
8.3%
t 128
 
2.4%
m 15
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 864
45.2%
B 460
24.1%
F 445
23.3%
S 128
 
6.7%
C 15
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 7301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

MasVnrArea
Real number (ℝ)

Distinct327
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:34.173041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2023-01-10T04:43:34.820211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.3%
180 8
 
0.6%
72 8
 
0.6%
108 8
 
0.6%
120 7
 
0.5%
16 7
 
0.5%
200 6
 
0.4%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
Other values (317) 529
36.4%
ValueCountFrequency (%)
0 861
59.3%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

ExterQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
906 
Gd
481 
Ex
 
51
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2904
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.4%
Gd 481
33.1%
Ex 51
 
3.5%
Fa 14
 
1.0%

Length

2023-01-10T04:43:35.421525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:35.984052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.4%
gd 481
33.1%
ex 51
 
3.5%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.2%
A 906
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2358
81.2%
Lowercase Letter 546
 
18.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 906
38.4%
A 906
38.4%
G 481
20.4%
E 51
 
2.2%
F 14
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
d 481
88.1%
x 51
 
9.3%
a 14
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 906
31.2%
A 906
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 906
31.2%
A 906
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1274 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2904
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1274
87.7%
Gd 146
 
10.1%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2023-01-10T04:43:36.448329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:37.025030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1274
87.7%
gd 146
 
10.1%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1274
43.9%
A 1274
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2726
93.9%
Lowercase Letter 178
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1274
46.7%
A 1274
46.7%
G 146
 
5.4%
F 28
 
1.0%
E 3
 
0.1%
P 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 146
82.0%
a 28
 
15.7%
x 3
 
1.7%
o 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1274
43.9%
A 1274
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1274
43.9%
A 1274
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
PConc
639 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.518595
Min length4

Characters and Unicode

Total characters8013
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 639
44.0%
CBlock 634
43.7%
BrkTil 146
 
10.1%
Slab 24
 
1.7%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2023-01-10T04:43:37.548398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:38.192936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pconc 639
44.0%
cblock 634
43.7%
brktil 146
 
10.1%
slab 24
 
1.7%
stone 6
 
0.4%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1285
16.0%
C 1273
15.9%
c 1273
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 645
8.0%
P 639
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5142
64.2%
Uppercase Letter 2871
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1285
25.0%
c 1273
24.8%
l 804
15.6%
k 780
15.2%
n 645
12.5%
i 146
 
2.8%
r 146
 
2.8%
a 24
 
0.5%
b 24
 
0.5%
t 6
 
0.1%
Other values (2) 9
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1273
44.3%
B 780
27.2%
P 639
22.3%
T 146
 
5.1%
S 30
 
1.0%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8013
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1285
16.0%
C 1273
15.9%
c 1273
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 645
8.0%
P 639
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1285
16.0%
C 1273
15.9%
c 1273
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 645
8.0%
P 639
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtQual
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
648 
Gd
612 
Ex
120 
NoInfo
 
37
Fa
 
35

Length

Max length6
Median length2
Mean length2.1019284
Min length2

Characters and Unicode

Total characters3052
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 648
44.6%
Gd 612
42.1%
Ex 120
 
8.3%
NoInfo 37
 
2.5%
Fa 35
 
2.4%

Length

2023-01-10T04:43:38.761022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:39.402879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 648
44.6%
gd 612
42.1%
ex 120
 
8.3%
noinfo 37
 
2.5%
fa 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 612
20.1%
d 612
20.1%
E 120
 
3.9%
x 120
 
3.9%
o 74
 
2.4%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (3) 107
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2137
70.0%
Lowercase Letter 915
30.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 648
30.3%
A 648
30.3%
G 612
28.6%
E 120
 
5.6%
N 37
 
1.7%
I 37
 
1.7%
F 35
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
d 612
66.9%
x 120
 
13.1%
o 74
 
8.1%
n 37
 
4.0%
f 37
 
4.0%
a 35
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3052
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 612
20.1%
d 612
20.1%
E 120
 
3.9%
x 120
 
3.9%
o 74
 
2.4%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (3) 107
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 612
20.1%
d 612
20.1%
E 120
 
3.9%
x 120
 
3.9%
o 74
 
2.4%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (3) 107
 
3.5%

BsmtCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1304 
Gd
 
64
Fa
 
45
NoInfo
 
37
Po
 
2

Length

Max length6
Median length2
Mean length2.1019284
Min length2

Characters and Unicode

Total characters3052
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1304
89.8%
Gd 64
 
4.4%
Fa 45
 
3.1%
NoInfo 37
 
2.5%
Po 2
 
0.1%

Length

2023-01-10T04:43:39.948564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:40.580536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1304
89.8%
gd 64
 
4.4%
fa 45
 
3.1%
noinfo 37
 
2.5%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1304
42.7%
A 1304
42.7%
o 76
 
2.5%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (2) 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2793
91.5%
Lowercase Letter 259
 
8.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1304
46.7%
A 1304
46.7%
G 64
 
2.3%
F 45
 
1.6%
N 37
 
1.3%
I 37
 
1.3%
P 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
o 76
29.3%
d 64
24.7%
a 45
17.4%
n 37
14.3%
f 37
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3052
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1304
42.7%
A 1304
42.7%
o 76
 
2.5%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (2) 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1304
42.7%
A 1304
42.7%
o 76
 
2.5%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
N 37
 
1.2%
I 37
 
1.2%
n 37
 
1.2%
Other values (2) 39
 
1.3%

BsmtExposure
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
No
946 
Av
221 
Gd
133 
Mn
114 
NoInfo
 
38

Length

Max length6
Median length2
Mean length2.1046832
Min length2

Characters and Unicode

Total characters3056
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 946
65.2%
Av 221
 
15.2%
Gd 133
 
9.2%
Mn 114
 
7.9%
NoInfo 38
 
2.6%

Length

2023-01-10T04:43:41.109924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:41.749859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 946
65.2%
av 221
 
15.2%
gd 133
 
9.2%
mn 114
 
7.9%
noinfo 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
o 1022
33.4%
N 984
32.2%
A 221
 
7.2%
v 221
 
7.2%
n 152
 
5.0%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.7%
I 38
 
1.2%
f 38
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1566
51.2%
Uppercase Letter 1490
48.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1022
65.3%
v 221
 
14.1%
n 152
 
9.7%
d 133
 
8.5%
f 38
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
N 984
66.0%
A 221
 
14.8%
G 133
 
8.9%
M 114
 
7.7%
I 38
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1022
33.4%
N 984
32.2%
A 221
 
7.2%
v 221
 
7.2%
n 152
 
5.0%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.7%
I 38
 
1.2%
f 38
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1022
33.4%
N 984
32.2%
A 221
 
7.2%
v 221
 
7.2%
n 152
 
5.0%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.7%
I 38
 
1.2%
f 38
 
1.2%

BsmtFinType1
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
428 
GLQ
413 
ALQ
220 
BLQ
148 
Rec
132 
Other values (2)
111 

Length

Max length6
Median length3
Mean length3.0764463
Min length3

Characters and Unicode

Total characters4467
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 428
29.5%
GLQ 413
28.4%
ALQ 220
15.2%
BLQ 148
 
10.2%
Rec 132
 
9.1%
LwQ 74
 
5.1%
NoInfo 37
 
2.5%

Length

2023-01-10T04:43:42.276182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:42.921998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 428
29.5%
glq 413
28.4%
alq 220
15.2%
blq 148
 
10.2%
rec 132
 
9.1%
lwq 74
 
5.1%
noinfo 37
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 855
19.1%
Q 855
19.1%
n 465
10.4%
f 465
10.4%
U 428
9.6%
G 413
9.2%
A 220
 
4.9%
B 148
 
3.3%
R 132
 
3.0%
e 132
 
3.0%
Other values (5) 354
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3125
70.0%
Lowercase Letter 1342
30.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 855
27.4%
Q 855
27.4%
U 428
13.7%
G 413
13.2%
A 220
 
7.0%
B 148
 
4.7%
R 132
 
4.2%
N 37
 
1.2%
I 37
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
n 465
34.6%
f 465
34.6%
e 132
 
9.8%
c 132
 
9.8%
w 74
 
5.5%
o 74
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4467
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 855
19.1%
Q 855
19.1%
n 465
10.4%
f 465
10.4%
U 428
9.6%
G 413
9.2%
A 220
 
4.9%
B 148
 
3.3%
R 132
 
3.0%
e 132
 
3.0%
Other values (5) 354
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 855
19.1%
Q 855
19.1%
n 465
10.4%
f 465
10.4%
U 428
9.6%
G 413
9.2%
A 220
 
4.9%
B 148
 
3.3%
R 132
 
3.0%
e 132
 
3.0%
Other values (5) 354
7.9%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct633
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.97039
Minimum0
Maximum5644
Zeros465
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:43.566490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median381
Q3706.5
95-th percentile1271.8
Maximum5644
Range5644
Interquartile range (IQR)706.5

Descriptive statistics

Standard deviation455.36028
Coefficient of variation (CV)1.0302959
Kurtosis11.290116
Mean441.97039
Median Absolute Deviation (MAD)381
Skewness1.7028851
Sum641741
Variance207352.98
MonotonicityNot monotonic
2023-01-10T04:43:44.176851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 465
32.0%
24 12
 
0.8%
16 9
 
0.6%
616 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
662 5
 
0.3%
686 5
 
0.3%
697 4
 
0.3%
641 4
 
0.3%
Other values (623) 933
64.3%
ValueCountFrequency (%)
0 465
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType2
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
1248 
Rec
 
54
LwQ
 
46
NoInfo
 
38
BLQ
 
33
Other values (2)
 
33

Length

Max length6
Median length3
Mean length3.0785124
Min length3

Characters and Unicode

Total characters4470
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1248
86.0%
Rec 54
 
3.7%
LwQ 46
 
3.2%
NoInfo 38
 
2.6%
BLQ 33
 
2.3%
ALQ 19
 
1.3%
GLQ 14
 
1.0%

Length

2023-01-10T04:43:44.765252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:45.396850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 1248
86.0%
rec 54
 
3.7%
lwq 46
 
3.2%
noinfo 38
 
2.6%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 1286
28.8%
f 1286
28.8%
U 1248
27.9%
L 112
 
2.5%
Q 112
 
2.5%
o 76
 
1.7%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 46
 
1.0%
Other values (5) 142
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2802
62.7%
Uppercase Letter 1668
37.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 1248
74.8%
L 112
 
6.7%
Q 112
 
6.7%
R 54
 
3.2%
N 38
 
2.3%
I 38
 
2.3%
B 33
 
2.0%
A 19
 
1.1%
G 14
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n 1286
45.9%
f 1286
45.9%
o 76
 
2.7%
e 54
 
1.9%
c 54
 
1.9%
w 46
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1286
28.8%
f 1286
28.8%
U 1248
27.9%
L 112
 
2.5%
Q 112
 
2.5%
o 76
 
1.7%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 46
 
1.0%
Other values (5) 142
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1286
28.8%
f 1286
28.8%
U 1248
27.9%
L 112
 
2.5%
Q 112
 
2.5%
o 76
 
1.7%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 46
 
1.0%
Other values (5) 142
 
3.2%

BsmtFinSF2
Real number (ℝ)

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.805785
Minimum0
Maximum1474
Zeros1285
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:46.012606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile397.8
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.72624
Coefficient of variation (CV)3.4552618
Kurtosis19.981146
Mean46.805785
Median Absolute Deviation (MAD)0
Skewness4.2419023
Sum67962
Variance26155.376
MonotonicityNot monotonic
2023-01-10T04:43:46.659111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1285
88.5%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
10.0%
ValueCountFrequency (%)
0 1285
88.5%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct777
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.07094
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:47.335253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1222.5
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585.5

Descriptive statistics

Standard deviation442.08293
Coefficient of variation (CV)0.77959017
Kurtosis0.47776449
Mean567.07094
Median Absolute Deviation (MAD)288
Skewness0.92093805
Sum823387
Variance195437.31
MonotonicityNot monotonic
2023-01-10T04:43:47.983656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.6%
300 7
 
0.5%
572 7
 
0.5%
600 7
 
0.5%
440 6
 
0.4%
270 6
 
0.4%
280 6
 
0.4%
672 6
 
0.4%
Other values (767) 1272
87.6%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct717
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1055.8471
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:48.628810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile513.7
Q1794.75
median990.5
Q31297.25
95-th percentile1748.7
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.11909
Coefficient of variation (CV)0.41494558
Kurtosis13.412402
Mean1055.8471
Median Absolute Deviation (MAD)234.5
Skewness1.5330401
Sum1533090
Variance191948.34
MonotonicityNot monotonic
2023-01-10T04:43:49.251481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
728 12
 
0.8%
768 12
 
0.8%
848 11
 
0.8%
780 11
 
0.8%
Other values (707) 1275
87.8%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

Heating
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
GasA
1420 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006887
Min length4

Characters and Unicode

Total characters5809
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1420
97.8%
GasW 18
 
1.2%
Grav 7
 
0.5%
Wall 4
 
0.3%
OthW 2
 
0.1%
Floor 1
 
0.1%

Length

2023-01-10T04:43:49.837711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:50.451138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1420
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1449
24.9%
G 1445
24.9%
s 1438
24.8%
A 1420
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2917
50.2%
Uppercase Letter 2892
49.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1449
49.7%
s 1438
49.3%
l 9
 
0.3%
r 8
 
0.3%
v 7
 
0.2%
t 2
 
0.1%
h 2
 
0.1%
o 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
G 1445
50.0%
A 1420
49.1%
W 24
 
0.8%
O 2
 
0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5809
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1449
24.9%
G 1445
24.9%
s 1438
24.8%
A 1420
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1449
24.9%
G 1445
24.9%
s 1438
24.8%
A 1420
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Ex
734 
TA
427 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2904
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 734
50.6%
TA 427
29.4%
Gd 241
 
16.6%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2023-01-10T04:43:50.947071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:51.524078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ex 734
50.6%
ta 427
29.4%
gd 241
 
16.6%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1879
64.7%
Lowercase Letter 1025
35.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 734
39.1%
T 427
22.7%
A 427
22.7%
G 241
 
12.8%
F 49
 
2.6%
P 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x 734
71.6%
d 241
 
23.5%
a 49
 
4.8%
o 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
True
1357 
False
 
95
ValueCountFrequency (%)
True 1357
93.5%
False 95
 
6.5%
2023-01-10T04:43:52.058797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Electrical
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
SBrkr
1326 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length6
Median length5
Mean length4.9993113
Min length3

Characters and Unicode

Total characters7259
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1326
91.3%
FuseA 94
 
6.5%
FuseF 27
 
1.9%
FuseP 3
 
0.2%
Mix 1
 
0.1%
NoInfo 1
 
0.1%

Length

2023-01-10T04:43:53.318941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:53.940088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1326
91.3%
fusea 94
 
6.5%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%
noinfo 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2652
36.5%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (8) 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4356
60.0%
Uppercase Letter 2903
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2652
60.9%
k 1326
30.4%
u 124
 
2.8%
s 124
 
2.8%
e 124
 
2.8%
o 2
 
< 0.1%
i 1
 
< 0.1%
x 1
 
< 0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 1326
45.7%
B 1326
45.7%
F 151
 
5.2%
A 94
 
3.2%
P 3
 
0.1%
M 1
 
< 0.1%
N 1
 
< 0.1%
I 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7259
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2652
36.5%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (8) 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2652
36.5%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (8) 9
 
0.1%

1stFlrSF
Real number (ℝ)

Distinct749
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.2707
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:54.532025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.55
Q1882
median1086
Q31391
95-th percentile1826.9
Maximum4692
Range4358
Interquartile range (IQR)509

Descriptive statistics

Standard deviation385.01842
Coefficient of variation (CV)0.33154926
Kurtosis5.8298463
Mean1161.2707
Median Absolute Deviation (MAD)234.5
Skewness1.3733955
Sum1686165
Variance148239.19
MonotonicityNot monotonic
2023-01-10T04:43:55.149093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (739) 1330
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%
2392 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct414
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.94421
Minimum0
Maximum2065
Zeros824
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:55.780392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.45
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.37072
Coefficient of variation (CV)1.2577547
Kurtosis-0.54648923
Mean346.94421
Median Absolute Deviation (MAD)0
Skewness0.81448515
Sum503763
Variance190419.41
MonotonicityNot monotonic
2023-01-10T04:43:56.400659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 824
56.7%
728 10
 
0.7%
504 9
 
0.6%
672 8
 
0.6%
546 8
 
0.6%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
689 5
 
0.3%
862 5
 
0.3%
Other values (404) 563
38.8%
ValueCountFrequency (%)
0 824
56.7%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

Distinct24
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8767218
Minimum0
Maximum572
Zeros1426
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:56.957124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.754995
Coefficient of variation (CV)8.2962913
Kurtosis82.753708
Mean5.8767218
Median Absolute Deviation (MAD)0
Skewness8.985769
Sum8533
Variance2377.0496
MonotonicityNot monotonic
2023-01-10T04:43:57.465161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1426
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1426
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

Distinct858
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514.0916
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:43:58.086112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11128
median1461.5
Q31776
95-th percentile2463.8
Maximum5642
Range5308
Interquartile range (IQR)648

Descriptive statistics

Standard deviation525.62777
Coefficient of variation (CV)0.34715718
Kurtosis4.9331871
Mean1514.0916
Median Absolute Deviation (MAD)326
Skewness1.3743752
Sum2198461
Variance276284.55
MonotonicityNot monotonic
2023-01-10T04:43:58.738720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
912 9
 
0.6%
1092 8
 
0.6%
1200 8
 
0.6%
816 8
 
0.6%
1728 7
 
0.5%
Other values (848) 1345
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
854 
1
582 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Length

2023-01-10T04:43:59.294274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:43:59.852159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 854
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
1370 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2023-01-10T04:44:00.314010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:00.855498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1370
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
2
762 
1
649 
3
 
32
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Length

2023-01-10T04:44:01.295736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:01.857555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 762
52.5%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
910 
1
530 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

Length

2023-01-10T04:44:02.321104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:02.867528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 910
62.7%
1 530
36.5%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8670799
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:03.280921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81481223
Coefficient of variation (CV)0.28419586
Kurtosis2.2581473
Mean2.8670799
Median Absolute Deviation (MAD)0
Skewness0.21758104
Sum4163
Variance0.66391897
MonotonicityNot monotonic
2023-01-10T04:44:03.728668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 801
55.2%
2 356
24.5%
4 211
 
14.5%
1 49
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 49
 
3.4%
2 356
24.5%
3 801
55.2%
4 211
 
14.5%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 211
 
14.5%
3 801
55.2%
2 356
24.5%
1 49
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
1
1385 
2
 
64
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Length

2023-01-10T04:44:04.250794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:04.807732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1385
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

KitchenQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
734 
Gd
580 
Ex
99 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2904
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 734
50.6%
Gd 580
39.9%
Ex 99
 
6.8%
Fa 39
 
2.7%

Length

2023-01-10T04:44:05.258935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:05.832897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 734
50.6%
gd 580
39.9%
ex 99
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 580
20.0%
d 580
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2186
75.3%
Lowercase Letter 718
 
24.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 734
33.6%
A 734
33.6%
G 580
26.5%
E 99
 
4.5%
F 39
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 580
80.8%
x 99
 
13.8%
a 39
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 580
20.0%
d 580
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 580
20.0%
d 580
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5172176
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:06.251081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6260647
Coefficient of variation (CV)0.24950291
Kurtosis0.88802334
Mean6.5172176
Median Absolute Deviation (MAD)1
Skewness0.68043801
Sum9463
Variance2.6440865
MonotonicityNot monotonic
2023-01-10T04:44:06.723043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 401
27.6%
7 326
22.5%
5 274
18.9%
8 186
12.8%
4 96
 
6.6%
9 74
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 96
 
6.6%
5 274
18.9%
6 401
27.6%
7 326
22.5%
8 186
12.8%
9 74
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 74
 
5.1%
8 186
12.8%
7 326
22.5%
6 401
27.6%
5 274
18.9%
4 96
 
6.6%

Functional
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Typ
1353 
Min2
 
34
Min1
 
31
Mod
 
15
Maj1
 
13
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.0571625
Min length3

Characters and Unicode

Total characters4439
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1353
93.2%
Min2 34
 
2.3%
Min1 31
 
2.1%
Mod 15
 
1.0%
Maj1 13
 
0.9%
Maj2 5
 
0.3%
Sev 1
 
0.1%

Length

2023-01-10T04:44:07.240523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:07.864977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
typ 1353
93.2%
min2 34
 
2.3%
min1 31
 
2.1%
mod 15
 
1.0%
maj1 13
 
0.9%
maj2 5
 
0.3%
sev 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1353
30.5%
y 1353
30.5%
p 1353
30.5%
M 98
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 18
 
0.4%
j 18
 
0.4%
Other values (5) 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2904
65.4%
Uppercase Letter 1452
32.7%
Decimal Number 83
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 1353
46.6%
p 1353
46.6%
i 65
 
2.2%
n 65
 
2.2%
a 18
 
0.6%
j 18
 
0.6%
o 15
 
0.5%
d 15
 
0.5%
e 1
 
< 0.1%
v 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 1353
93.2%
M 98
 
6.7%
S 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 44
53.0%
2 39
47.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4356
98.1%
Common 83
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1353
31.1%
y 1353
31.1%
p 1353
31.1%
M 98
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
a 18
 
0.4%
j 18
 
0.4%
o 15
 
0.3%
d 15
 
0.3%
Other values (3) 3
 
0.1%
Common
ValueCountFrequency (%)
1 44
53.0%
2 39
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1353
30.5%
y 1353
30.5%
p 1353
30.5%
M 98
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 44
 
1.0%
2 39
 
0.9%
a 18
 
0.4%
j 18
 
0.4%
Other values (5) 33
 
0.7%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
686 
1
648 
2
113 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

Length

2023-01-10T04:44:08.378977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:08.939075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 686
47.2%
1 648
44.6%
2 113
 
7.8%
3 5
 
0.3%

FireplaceQu
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoInfo
686 
Gd
378 
TA
311 
Fa
 
33
Ex
 
24

Length

Max length6
Median length2
Mean length3.8898072
Min length2

Characters and Unicode

Total characters5648
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoInfo
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
NoInfo 686
47.2%
Gd 378
26.0%
TA 311
21.4%
Fa 33
 
2.3%
Ex 24
 
1.7%
Po 20
 
1.4%

Length

2023-01-10T04:44:09.430433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:10.072752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 686
47.2%
gd 378
26.0%
ta 311
21.4%
fa 33
 
2.3%
ex 24
 
1.7%
po 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o 1392
24.6%
N 686
12.1%
I 686
12.1%
n 686
12.1%
f 686
12.1%
G 378
 
6.7%
d 378
 
6.7%
T 311
 
5.5%
A 311
 
5.5%
F 33
 
0.6%
Other values (4) 101
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3199
56.6%
Uppercase Letter 2449
43.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 686
28.0%
I 686
28.0%
G 378
15.4%
T 311
12.7%
A 311
12.7%
F 33
 
1.3%
E 24
 
1.0%
P 20
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
o 1392
43.5%
n 686
21.4%
f 686
21.4%
d 378
 
11.8%
a 33
 
1.0%
x 24
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1392
24.6%
N 686
12.1%
I 686
12.1%
n 686
12.1%
f 686
12.1%
G 378
 
6.7%
d 378
 
6.7%
T 311
 
5.5%
A 311
 
5.5%
F 33
 
0.6%
Other values (4) 101
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1392
24.6%
N 686
12.1%
I 686
12.1%
n 686
12.1%
f 686
12.1%
G 378
 
6.7%
d 378
 
6.7%
T 311
 
5.5%
A 311
 
5.5%
F 33
 
0.6%
Other values (4) 101
 
1.8%

GarageType
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Attchd
863 
Detchd
387 
BuiltIn
87 
NoInfo
 
81
Basment
 
19
Other values (2)
 
15

Length

Max length7
Median length6
Mean length6.0792011
Min length6

Characters and Unicode

Total characters8827
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 863
59.4%
Detchd 387
26.7%
BuiltIn 87
 
6.0%
NoInfo 81
 
5.6%
Basment 19
 
1.3%
CarPort 9
 
0.6%
2Types 6
 
0.4%

Length

2023-01-10T04:44:10.601472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:11.232294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
attchd 863
59.4%
detchd 387
26.7%
builtin 87
 
6.0%
noinfo 81
 
5.6%
basment 19
 
1.3%
carport 9
 
0.6%
2types 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 2228
25.2%
c 1250
14.2%
h 1250
14.2%
d 1250
14.2%
A 863
 
9.8%
e 412
 
4.7%
D 387
 
4.4%
n 187
 
2.1%
o 171
 
1.9%
I 168
 
1.9%
Other values (16) 661
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7192
81.5%
Uppercase Letter 1629
 
18.5%
Decimal Number 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2228
31.0%
c 1250
17.4%
h 1250
17.4%
d 1250
17.4%
e 412
 
5.7%
n 187
 
2.6%
o 171
 
2.4%
u 87
 
1.2%
i 87
 
1.2%
l 87
 
1.2%
Other values (7) 183
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
A 863
53.0%
D 387
23.8%
I 168
 
10.3%
B 106
 
6.5%
N 81
 
5.0%
C 9
 
0.6%
P 9
 
0.6%
T 6
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8821
99.9%
Common 6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2228
25.3%
c 1250
14.2%
h 1250
14.2%
d 1250
14.2%
A 863
 
9.8%
e 412
 
4.7%
D 387
 
4.4%
n 187
 
2.1%
o 171
 
1.9%
I 168
 
1.9%
Other values (15) 655
 
7.4%
Common
ValueCountFrequency (%)
2 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2228
25.2%
c 1250
14.2%
h 1250
14.2%
d 1250
14.2%
A 863
 
9.8%
e 412
 
4.7%
D 387
 
4.4%
n 187
 
2.1%
o 171
 
1.9%
I 168
 
1.9%
Other values (16) 661
 
7.5%

GarageYrBlt
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size22.7 KiB

GarageFinish
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
605 
RFn
418 
Fin
348 
NoInfo
81 

Length

Max length6
Median length3
Mean length3.1673554
Min length3

Characters and Unicode

Total characters4599
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 605
41.7%
RFn 418
28.8%
Fin 348
24.0%
NoInfo 81
 
5.6%

Length

2023-01-10T04:44:11.792246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:12.395370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 605
41.7%
rfn 418
28.8%
fin 348
24.0%
noinfo 81
 
5.6%

Most occurring characters

ValueCountFrequency (%)
n 1452
31.6%
F 766
16.7%
f 686
14.9%
U 605
13.2%
R 418
 
9.1%
i 348
 
7.6%
o 162
 
3.5%
N 81
 
1.8%
I 81
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2648
57.6%
Uppercase Letter 1951
42.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 766
39.3%
U 605
31.0%
R 418
21.4%
N 81
 
4.2%
I 81
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
n 1452
54.8%
f 686
25.9%
i 348
 
13.1%
o 162
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4599
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1452
31.6%
F 766
16.7%
f 686
14.9%
U 605
13.2%
R 418
 
9.1%
i 348
 
7.6%
o 162
 
3.5%
N 81
 
1.8%
I 81
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1452
31.6%
F 766
16.7%
f 686
14.9%
U 605
13.2%
R 418
 
9.1%
i 348
 
7.6%
o 162
 
3.5%
N 81
 
1.8%
I 81
 
1.8%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
2
817 
1
369 
3
180 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Length

2023-01-10T04:44:12.871026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:13.450092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 817
56.3%
1 369
25.4%
3 180
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct438
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.47521
Minimum0
Maximum1418
Zeros81
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:14.031114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1327.75
median478
Q3576
95-th percentile848.7
Maximum1418
Range1418
Interquartile range (IQR)248.25

Descriptive statistics

Standard deviation214.1064
Coefficient of variation (CV)0.45315901
Kurtosis0.91151914
Mean472.47521
Median Absolute Deviation (MAD)118
Skewness0.18330012
Sum686034
Variance45841.549
MonotonicityNot monotonic
2023-01-10T04:44:14.694371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.6%
440 48
 
3.3%
576 47
 
3.2%
240 38
 
2.6%
484 33
 
2.3%
528 33
 
2.3%
288 27
 
1.9%
400 25
 
1.7%
264 24
 
1.7%
480 23
 
1.6%
Other values (428) 1073
73.9%
ValueCountFrequency (%)
0 81
5.6%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

GarageQual
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1303 
NoInfo
 
81
Fa
 
48
Gd
 
14
Ex
 
3

Length

Max length6
Median length2
Mean length2.2231405
Min length2

Characters and Unicode

Total characters3228
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1303
89.7%
NoInfo 81
 
5.6%
Fa 48
 
3.3%
Gd 14
 
1.0%
Ex 3
 
0.2%
Po 3
 
0.2%

Length

2023-01-10T04:44:15.341194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:15.995477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1303
89.7%
noinfo 81
 
5.6%
fa 48
 
3.3%
gd 14
 
1.0%
ex 3
 
0.2%
po 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 1303
40.4%
A 1303
40.4%
o 165
 
5.1%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 48
 
1.5%
a 48
 
1.5%
G 14
 
0.4%
Other values (4) 23
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2836
87.9%
Lowercase Letter 392
 
12.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1303
45.9%
A 1303
45.9%
N 81
 
2.9%
I 81
 
2.9%
F 48
 
1.7%
G 14
 
0.5%
E 3
 
0.1%
P 3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
o 165
42.1%
n 81
20.7%
f 81
20.7%
a 48
 
12.2%
d 14
 
3.6%
x 3
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1303
40.4%
A 1303
40.4%
o 165
 
5.1%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 48
 
1.5%
a 48
 
1.5%
G 14
 
0.4%
Other values (4) 23
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1303
40.4%
A 1303
40.4%
o 165
 
5.1%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 48
 
1.5%
a 48
 
1.5%
G 14
 
0.4%
Other values (4) 23
 
0.7%

GarageCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1318 
NoInfo
 
81
Fa
 
35
Gd
 
9
Po
 
7

Length

Max length6
Median length2
Mean length2.2231405
Min length2

Characters and Unicode

Total characters3228
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1318
90.8%
NoInfo 81
 
5.6%
Fa 35
 
2.4%
Gd 9
 
0.6%
Po 7
 
0.5%
Ex 2
 
0.1%

Length

2023-01-10T04:44:16.533486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:17.187996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1318
90.8%
noinfo 81
 
5.6%
fa 35
 
2.4%
gd 9
 
0.6%
po 7
 
0.5%
ex 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1318
40.8%
A 1318
40.8%
o 169
 
5.2%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 35
 
1.1%
a 35
 
1.1%
G 9
 
0.3%
Other values (4) 20
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2851
88.3%
Lowercase Letter 377
 
11.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1318
46.2%
A 1318
46.2%
N 81
 
2.8%
I 81
 
2.8%
F 35
 
1.2%
G 9
 
0.3%
P 7
 
0.2%
E 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
o 169
44.8%
n 81
21.5%
f 81
21.5%
a 35
 
9.3%
d 9
 
2.4%
x 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1318
40.8%
A 1318
40.8%
o 169
 
5.2%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 35
 
1.1%
a 35
 
1.1%
G 9
 
0.3%
Other values (4) 20
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1318
40.8%
A 1318
40.8%
o 169
 
5.2%
N 81
 
2.5%
I 81
 
2.5%
n 81
 
2.5%
f 81
 
2.5%
F 35
 
1.1%
a 35
 
1.1%
G 9
 
0.3%
Other values (4) 20
 
0.6%

PavedDrive
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Y
1332 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1452
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1332
91.7%
N 90
 
6.2%
P 30
 
2.1%

Length

2023-01-10T04:44:17.680139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:18.219470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
y 1332
91.7%
n 90
 
6.2%
p 30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 1332
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1452
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1332
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1452
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1332
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1332
91.7%
N 90
 
6.2%
P 30
 
2.1%

WoodDeckSF
Real number (ℝ)

Distinct274
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.416667
Minimum0
Maximum857
Zeros755
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:18.758440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.39371
Coefficient of variation (CV)1.3280887
Kurtosis3.0011805
Mean94.416667
Median Absolute Deviation (MAD)0
Skewness1.5423064
Sum137093
Variance15723.582
MonotonicityNot monotonic
2023-01-10T04:44:19.413240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 755
52.0%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
240 10
 
0.7%
208 10
 
0.7%
Other values (264) 482
33.2%
ValueCountFrequency (%)
0 755
52.0%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

Distinct201
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.390496
Minimum0
Maximum547
Zeros654
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:20.059273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q368
95-th percentile172.9
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.048619
Coefficient of variation (CV)1.4237533
Kurtosis8.6698674
Mean46.390496
Median Absolute Deviation (MAD)24
Skewness2.3857254
Sum67359
Variance4362.4201
MonotonicityNot monotonic
2023-01-10T04:44:20.666790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 654
45.0%
36 29
 
2.0%
48 21
 
1.4%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (191) 628
43.3%
ValueCountFrequency (%)
0 654
45.0%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.6%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

Distinct119
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.937328
Minimum0
Maximum552
Zeros1245
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:21.322428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.089879
Coefficient of variation (CV)2.7847457
Kurtosis10.484432
Mean21.937328
Median Absolute Deviation (MAD)0
Skewness3.0953634
Sum31853
Variance3731.9733
MonotonicityNot monotonic
2023-01-10T04:44:22.738843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1245
85.7%
112 15
 
1.0%
96 6
 
0.4%
144 5
 
0.3%
192 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
252 4
 
0.3%
116 4
 
0.3%
156 4
 
0.3%
Other values (109) 154
 
10.6%
ValueCountFrequency (%)
0 1245
85.7%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4283747
Minimum0
Maximum508
Zeros1428
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:23.296562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.396943
Coefficient of variation (CV)8.5746005
Kurtosis122.9631
Mean3.4283747
Median Absolute Deviation (MAD)0
Skewness10.275369
Sum4978
Variance864.18026
MonotonicityNot monotonic
2023-01-10T04:44:23.819880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1428
98.3%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1428
98.3%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.143939
Minimum0
Maximum480
Zeros1336
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:24.423571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.899665
Coefficient of variation (CV)3.6912235
Kurtosis18.31459
Mean15.143939
Median Absolute Deviation (MAD)0
Skewness4.1090581
Sum21989
Variance3124.7725
MonotonicityNot monotonic
2023-01-10T04:44:25.112649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1336
92.0%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1336
92.0%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7741047
Minimum0
Maximum738
Zeros1445
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:25.642169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.287389
Coefficient of variation (CV)14.522663
Kurtosis222.02218
Mean2.7741047
Median Absolute Deviation (MAD)0
Skewness14.787221
Sum4028
Variance1623.0737
MonotonicityNot monotonic
2023-01-10T04:44:26.071391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1445
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1445
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1445
99.5%

PoolQC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoInfo
1445 
Gd
 
3
Ex
 
2
Fa
 
2

Length

Max length6
Median length6
Mean length5.9807163
Min length2

Characters and Unicode

Total characters8684
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoInfo
2nd rowNoInfo
3rd rowNoInfo
4th rowNoInfo
5th rowNoInfo

Common Values

ValueCountFrequency (%)
NoInfo 1445
99.5%
Gd 3
 
0.2%
Ex 2
 
0.1%
Fa 2
 
0.1%

Length

2023-01-10T04:44:26.654340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:27.278521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 1445
99.5%
gd 3
 
0.2%
ex 2
 
0.1%
fa 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 2890
33.3%
N 1445
16.6%
I 1445
16.6%
n 1445
16.6%
f 1445
16.6%
G 3
 
< 0.1%
d 3
 
< 0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
F 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5787
66.6%
Uppercase Letter 2897
33.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2890
49.9%
n 1445
25.0%
f 1445
25.0%
d 3
 
0.1%
x 2
 
< 0.1%
a 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1445
49.9%
I 1445
49.9%
G 3
 
0.1%
E 2
 
0.1%
F 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2890
33.3%
N 1445
16.6%
I 1445
16.6%
n 1445
16.6%
f 1445
16.6%
G 3
 
< 0.1%
d 3
 
< 0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
F 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2890
33.3%
N 1445
16.6%
I 1445
16.6%
n 1445
16.6%
f 1445
16.6%
G 3
 
< 0.1%
d 3
 
< 0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
F 2
 
< 0.1%

Fence
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoInfo
1171 
MnPrv
157 
GdPrv
 
59
GdWo
 
54
MnWw
 
11

Length

Max length6
Median length6
Mean length5.761708
Min length4

Characters and Unicode

Total characters8366
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoInfo
2nd rowNoInfo
3rd rowNoInfo
4th rowNoInfo
5th rowNoInfo

Common Values

ValueCountFrequency (%)
NoInfo 1171
80.6%
MnPrv 157
 
10.8%
GdPrv 59
 
4.1%
GdWo 54
 
3.7%
MnWw 11
 
0.8%

Length

2023-01-10T04:44:27.776392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:28.417819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 1171
80.6%
mnprv 157
 
10.8%
gdprv 59
 
4.1%
gdwo 54
 
3.7%
mnww 11
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o 2396
28.6%
n 1339
16.0%
N 1171
14.0%
I 1171
14.0%
f 1171
14.0%
P 216
 
2.6%
r 216
 
2.6%
v 216
 
2.6%
M 168
 
2.0%
G 113
 
1.4%
Other values (3) 189
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5462
65.3%
Uppercase Letter 2904
34.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2396
43.9%
n 1339
24.5%
f 1171
21.4%
r 216
 
4.0%
v 216
 
4.0%
d 113
 
2.1%
w 11
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 1171
40.3%
I 1171
40.3%
P 216
 
7.4%
M 168
 
5.8%
G 113
 
3.9%
W 65
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8366
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2396
28.6%
n 1339
16.0%
N 1171
14.0%
I 1171
14.0%
f 1171
14.0%
P 216
 
2.6%
r 216
 
2.6%
v 216
 
2.6%
M 168
 
2.0%
G 113
 
1.4%
Other values (3) 189
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2396
28.6%
n 1339
16.0%
N 1171
14.0%
I 1171
14.0%
f 1171
14.0%
P 216
 
2.6%
r 216
 
2.6%
v 216
 
2.6%
M 168
 
2.0%
G 113
 
1.4%
Other values (3) 189
 
2.3%

MiscFeature
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoInfo
1398 
Shed
 
49
Gar2
 
2
Othr
 
2
TenC
 
1

Length

Max length6
Median length6
Mean length5.9256198
Min length4

Characters and Unicode

Total characters8604
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNoInfo
2nd rowNoInfo
3rd rowNoInfo
4th rowNoInfo
5th rowNoInfo

Common Values

ValueCountFrequency (%)
NoInfo 1398
96.3%
Shed 49
 
3.4%
Gar2 2
 
0.1%
Othr 2
 
0.1%
TenC 1
 
0.1%

Length

2023-01-10T04:44:28.950863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:29.597029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 1398
96.3%
shed 49
 
3.4%
gar2 2
 
0.1%
othr 2
 
0.1%
tenc 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 2796
32.5%
n 1399
16.3%
N 1398
16.2%
I 1398
16.2%
f 1398
16.2%
h 51
 
0.6%
e 50
 
0.6%
d 49
 
0.6%
S 49
 
0.6%
r 4
 
< 0.1%
Other values (7) 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5751
66.8%
Uppercase Letter 2851
33.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2796
48.6%
n 1399
24.3%
f 1398
24.3%
h 51
 
0.9%
e 50
 
0.9%
d 49
 
0.9%
r 4
 
0.1%
a 2
 
< 0.1%
t 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1398
49.0%
I 1398
49.0%
S 49
 
1.7%
G 2
 
0.1%
O 2
 
0.1%
T 1
 
< 0.1%
C 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8602
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2796
32.5%
n 1399
16.3%
N 1398
16.3%
I 1398
16.3%
f 1398
16.3%
h 51
 
0.6%
e 50
 
0.6%
d 49
 
0.6%
S 49
 
0.6%
r 4
 
< 0.1%
Other values (6) 10
 
0.1%
Common
ValueCountFrequency (%)
2 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2796
32.5%
n 1399
16.3%
N 1398
16.2%
I 1398
16.2%
f 1398
16.2%
h 51
 
0.6%
e 50
 
0.6%
d 49
 
0.6%
S 49
 
0.6%
r 4
 
< 0.1%
Other values (7) 12
 
0.1%

MiscVal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.72865
Minimum0
Maximum15500
Zeros1400
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:30.075684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation497.47828
Coefficient of variation (CV)11.376484
Kurtosis697.17112
Mean43.72865
Median Absolute Deviation (MAD)0
Skewness24.409889
Sum63494
Variance247484.64
MonotonicityNot monotonic
2023-01-10T04:44:30.596274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1400
96.4%
400 11
 
0.8%
500 8
 
0.6%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1400
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.6%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3181818
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:31.148528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6996439
Coefficient of variation (CV)0.42728177
Kurtosis-0.39691869
Mean6.3181818
Median Absolute Deviation (MAD)2
Skewness0.2102081
Sum9174
Variance7.2880772
MonotonicityNot monotonic
2023-01-10T04:44:31.601617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.4%
7 234
16.1%
5 202
13.9%
4 141
9.7%
8 121
8.3%
3 104
7.2%
10 89
 
6.1%
11 78
 
5.4%
9 62
 
4.3%
12 58
 
4.0%
Other values (2) 110
7.6%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 104
7.2%
4 141
9.7%
5 202
13.9%
6 253
17.4%
7 234
16.1%
8 121
8.3%
9 62
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 58
 
4.0%
11 78
 
5.4%
10 89
 
6.1%
9 62
 
4.3%
8 121
8.3%
7 234
16.1%
6 253
17.4%
5 202
13.9%
4 141
9.7%
3 104
7.2%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
2009
337 
2007
327 
2006
313 
2008
301 
2010
174 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5808
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 337
23.2%
2007 327
22.5%
2006 313
21.6%
2008 301
20.7%
2010 174
12.0%

Length

2023-01-10T04:44:32.094650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:32.682800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 337
23.2%
2007 327
22.5%
2006 313
21.6%
2008 301
20.7%
2010 174
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2904
50.0%
2 1452
25.0%
9 337
 
5.8%
7 327
 
5.6%
6 313
 
5.4%
8 301
 
5.2%
1 174
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2904
50.0%
2 1452
25.0%
9 337
 
5.8%
7 327
 
5.6%
6 313
 
5.4%
8 301
 
5.2%
1 174
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2904
50.0%
2 1452
25.0%
9 337
 
5.8%
7 327
 
5.6%
6 313
 
5.4%
8 301
 
5.2%
1 174
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2904
50.0%
2 1452
25.0%
9 337
 
5.8%
7 327
 
5.6%
6 313
 
5.4%
8 301
 
5.2%
1 174
 
3.0%

SaleType
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
WD
1262 
New
 
119
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.1570248
Min length2

Characters and Unicode

Total characters3132
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1262
86.9%
New 119
 
8.2%
COD 43
 
3.0%
ConLD 9
 
0.6%
ConLI 5
 
0.3%
ConLw 5
 
0.3%
CWD 4
 
0.3%
Oth 3
 
0.2%
Con 2
 
0.1%

Length

2023-01-10T04:44:33.220190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:33.883888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
wd 1262
86.9%
new 119
 
8.2%
cod 43
 
3.0%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1318
42.1%
W 1266
40.4%
w 124
 
4.0%
N 119
 
3.8%
e 119
 
3.8%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2841
90.7%
Lowercase Letter 291
 
9.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1318
46.4%
W 1266
44.6%
N 119
 
4.2%
C 68
 
2.4%
O 46
 
1.6%
L 19
 
0.7%
I 5
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
w 124
42.6%
e 119
40.9%
o 21
 
7.2%
n 21
 
7.2%
t 3
 
1.0%
h 3
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1318
42.1%
W 1266
40.4%
w 124
 
4.0%
N 119
 
3.8%
e 119
 
3.8%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1318
42.1%
W 1266
40.4%
w 124
 
4.0%
N 119
 
3.8%
e 119
 
3.8%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.4%

SaleCondition
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Normal
1194 
Partial
122 
Abnorml
 
101
Family
 
20
Alloca
 
11

Length

Max length7
Median length6
Mean length6.1563361
Min length6

Characters and Unicode

Total characters8939
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1194
82.2%
Partial 122
 
8.4%
Abnorml 101
 
7.0%
Family 20
 
1.4%
Alloca 11
 
0.8%
AdjLand 4
 
0.3%

Length

2023-01-10T04:44:34.460842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T04:44:35.069669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 1194
82.2%
partial 122
 
8.4%
abnorml 101
 
7.0%
family 20
 
1.4%
alloca 11
 
0.8%
adjland 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1473
16.5%
l 1459
16.3%
r 1417
15.9%
m 1315
14.7%
o 1306
14.6%
N 1194
13.4%
i 142
 
1.6%
P 122
 
1.4%
t 122
 
1.4%
A 116
 
1.3%
Other values (8) 273
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7483
83.7%
Uppercase Letter 1456
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1473
19.7%
l 1459
19.5%
r 1417
18.9%
m 1315
17.6%
o 1306
17.5%
i 142
 
1.9%
t 122
 
1.6%
n 105
 
1.4%
b 101
 
1.3%
y 20
 
0.3%
Other values (3) 23
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 1194
82.0%
P 122
 
8.4%
A 116
 
8.0%
F 20
 
1.4%
L 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 8939
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1473
16.5%
l 1459
16.3%
r 1417
15.9%
m 1315
14.7%
o 1306
14.6%
N 1194
13.4%
i 142
 
1.6%
P 122
 
1.4%
t 122
 
1.4%
A 116
 
1.3%
Other values (8) 273
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1473
16.5%
l 1459
16.3%
r 1417
15.9%
m 1315
14.7%
o 1306
14.6%
N 1194
13.4%
i 142
 
1.6%
P 122
 
1.4%
t 122
 
1.4%
A 116
 
1.3%
Other values (8) 273
 
3.1%

SalePrice
Real number (ℝ)

Distinct657
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180615.06
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2023-01-10T04:44:35.689775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129900
median162700
Q3214000
95-th percentile325793.2
Maximum755000
Range720100
Interquartile range (IQR)84100

Descriptive statistics

Standard deviation79285.541
Coefficient of variation (CV)0.43897524
Kurtosis6.5801085
Mean180615.06
Median Absolute Deviation (MAD)37700
Skewness1.8840445
Sum2.6225307 × 108
Variance6.2861971 × 109
MonotonicityNot monotonic
2023-01-10T04:44:36.349899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
145000 14
 
1.0%
155000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (647) 1315
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2023-01-10T04:42:43.611816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:42.798154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:58.430805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:12.991526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:28.281976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:42.560153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:56.774695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:13.042819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:28.208041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:44.019616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:59.572346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:14.138704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:30.138449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:44.278677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:59.899558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:13.779387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:27.942646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:43.569743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:57.824411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:13.191370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:28.816571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:43.985869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:58.916170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:13.465441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:28.019722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:43.291132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:57.817444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:12.918647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:29.254923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:44.162449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:43.335360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:58.969588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:13.525603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:28.820055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:43.073551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:57.336293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:13.590871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:28.752542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:44.569486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:00.106923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:14.718021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:30.648860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:44.821873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:00.408062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:14.302014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:28.474949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:44.087799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:58.376969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:13.732996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:29.369831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:44.514225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:59.457054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:13.999964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:28.551184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:43.823307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:58.362756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:13.481348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:29.775992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:44.629446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:44.380075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:59.405812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:13.967663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:29.250533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:43.507282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:57.793503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:14.034530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:29.219933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:45.037304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:00.537204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:15.165975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:31.084237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:45.267053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:00.814647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:14.739980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:28.925899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:44.518347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:58.841729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:14.177516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:29.821229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:44.934847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:59.887463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:14.434579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:28.981628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:44.267075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:58.824141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:13.954361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:30.211708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:45.150675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:44.891167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:59.893087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:14.443819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:29.736673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:43.985658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:59.145447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:14.550889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:29.719364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:45.555142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:01.041370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:16.487117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:31.558704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:45.776353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:01.298701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:15.225160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:29.424300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:44.991818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:59.363979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:15.524207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:30.337273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:45.415437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:00.377001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:14.964209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:29.489699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:44.751984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:59.350993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:14.485281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:30.702954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:45.637445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:45.387800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:00.362702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:14.955792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:30.184448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:44.444584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:59.639905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:15.059651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:30.208018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:46.059644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:01.504954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:16.980000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:32.016326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-10T04:36:26.199000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:40.501237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:54.721180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:10.796962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:25.903967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:41.859184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:57.343586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:12.037696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:27.950747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:42.226941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:57.750364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:11.752101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:25.889136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:41.417050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:55.766036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:10.958123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:26.686069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:41.802372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:56.868192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:11.340422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:25.919879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:41.193862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:55.720267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:10.738701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:27.033868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:41.543359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:58.005349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:56.808557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:11.417529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:26.723213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:41.016816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:55.248389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:11.371403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:26.448646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:42.400951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:57.907791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:12.569327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:28.494301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:42.749263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:58.287450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:12.270209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:26.407526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:41.948814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:56.282285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:11.518918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:27.226951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:42.346451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:57.385352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:11.876098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:26.446439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:41.714251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:56.240284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:11.276103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:27.590599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:42.062954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:58.576590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:57.379074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:11.970137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:27.267203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:41.559794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:55.781788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:11.957339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:27.089317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:42.971797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:58.482958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:13.114673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:29.072055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:43.280404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:58.848721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:12.794031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:26.948290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:42.517545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:56.823591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:12.111302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:27.781206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:42.922023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:57.920027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:12.433531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:26.995888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:42.268359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:56.795707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:11.857829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:28.166784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:42.610169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:59.090086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:35:57.884149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:12.458922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:27.753248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:42.034174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:36:56.255939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:12.483655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:27.639376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:43.468459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:37:59.007973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:13.607854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:29.577912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:43.758208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:38:59.347311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:13.267745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:27.417906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:43.013550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:39:57.296348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:12.620877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:28.277114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:43.434265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:40:58.397926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:12.931433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:27.488068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:42.752092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:41:57.280320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:12.363591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:28.683101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T04:42:43.080295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-10T04:44:37.528437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBedroomAbvGrTotRmsAbvGrdGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldSalePriceMSZoningStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalBsmtFullBathBsmtHalfBathFullBathHalfBathKitchenAbvGrKitchenQualFunctionalFireplacesFireplaceQuGarageTypeGarageFinishGarageCarsGarageQualGarageCondPavedDrivePoolQCFenceMiscFeatureYrSoldSaleTypeSaleCondition
Id1.0000.019-0.018-0.006-0.0330.004-0.008-0.014-0.036-0.015-0.006-0.010-0.036-0.0030.011-0.0280.0030.0460.0280.004-0.042-0.007-0.005-0.0370.0060.056-0.0430.017-0.0200.0000.0000.0000.0090.0000.0100.0000.0250.0000.0000.0000.0000.0180.0500.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0400.0000.0380.0000.0000.0000.0000.0000.0000.0000.0380.0250.0000.0270.0080.0000.000
MSSubClass0.0191.0000.2600.2700.0280.078-0.168-0.118-0.041-0.146-0.048-0.015-0.226-0.1370.5280.0460.3490.4370.4320.0510.0070.0410.104-0.014-0.0120.0660.0290.0770.0890.3390.0910.3060.1660.1400.0000.0790.0860.3750.1110.1570.8910.8480.2170.0800.1930.2040.2300.2840.1670.3590.3340.2000.2620.2320.1420.1500.2380.4340.1610.2390.0840.3140.5340.4980.2820.0990.2220.1890.3310.3820.2950.2350.2120.3090.0930.1310.1790.0000.0900.156
LotFrontage-0.0180.2601.0000.6960.212-0.0450.1400.0730.2360.1620.0820.0930.3720.4350.059-0.0210.3820.3270.3630.3730.1240.168-0.0750.0760.0370.0860.0400.0170.4030.2060.3000.1240.2720.1910.0000.1270.3680.2240.0800.0420.3310.0370.1110.2780.0930.1030.1800.1200.0000.0820.1000.0000.1240.0530.0610.0000.0230.0370.0000.1640.0090.1210.0000.0000.0920.0000.2480.1020.1010.1190.1720.0000.0000.0510.3250.0000.0860.0130.0000.045
LotArea-0.0060.2700.6961.0000.233-0.0450.1050.0770.1780.1710.0730.0750.3640.4420.123-0.0200.4490.3390.4060.3680.1860.178-0.0700.0630.0930.0850.0600.0070.4580.0000.2910.0000.2660.2580.0000.0720.4510.1640.0000.0000.0360.0000.1140.2530.0210.0620.1030.0000.0000.0000.0000.0000.1250.0000.0570.0840.0000.0000.0000.2110.0000.0900.0000.0000.0000.0000.1570.0000.0450.0340.0130.0000.0000.0340.1280.0000.0770.0000.0000.000
OverallQual-0.0330.0280.2120.2331.000-0.1760.6460.5560.4140.130-0.1160.2740.4590.4080.292-0.0340.6040.1250.4300.5400.2620.433-0.1610.0340.0480.057-0.0870.0600.8090.1880.0720.0990.1180.1610.0000.0210.1520.3200.0620.1570.1300.1450.1190.1030.2010.1950.2810.6120.1950.2900.4570.3080.2080.2400.1360.1670.2580.3740.1400.0650.0620.4070.2240.1070.5380.1180.2680.2690.2240.3720.4010.1600.1640.1750.1090.1310.0000.0000.1610.151
OverallCond0.0040.078-0.045-0.045-0.1761.000-0.419-0.040-0.179-0.0060.102-0.129-0.214-0.164-0.0010.039-0.152-0.003-0.104-0.200-0.044-0.1320.1130.0320.074-0.0060.086-0.007-0.1270.1610.0680.1170.0630.1010.0000.0000.1880.2230.0510.0990.1270.1220.0440.0000.1900.1670.1680.3200.3800.2560.2830.4160.1040.1620.0750.0920.1770.3170.2400.0000.1010.3100.0770.0720.2460.1660.1040.0870.1670.2300.2190.1630.1360.1880.0000.1160.0160.0510.1040.114
YearBuilt-0.008-0.1680.1400.1050.646-0.4191.0000.6810.4020.190-0.1100.1390.4280.2940.030-0.1460.289-0.0320.1790.5280.2910.390-0.4080.023-0.0720.009-0.0910.0150.6520.2930.0000.3120.1750.1590.0000.1050.0970.4810.1190.1620.2500.2900.1600.0700.3340.3260.3080.4340.1880.5030.4560.1920.1900.3290.1500.1710.3350.4380.1650.1440.0880.3510.2260.2140.4010.0850.1700.2130.2650.3880.3400.2330.1980.3440.0000.1700.0460.0000.1560.197
YearRemodAdd-0.014-0.1180.0730.0770.556-0.0400.6811.0000.2340.063-0.1250.1770.2990.2410.074-0.0640.283-0.0520.2010.3970.2340.351-0.2330.053-0.0440.003-0.0900.0170.5700.1990.1100.1410.1400.1290.0790.0860.0820.3870.0790.0000.1960.2000.0810.0400.2840.2770.2810.3880.0980.3220.3420.1210.1500.2440.1200.0830.3260.3770.1950.1220.0760.2700.2000.1110.4160.0510.1370.2250.1880.2860.2760.1090.1080.1750.0190.1400.0620.0000.2060.256
MasVnrArea-0.036-0.0410.2360.1780.414-0.1790.4020.2341.0000.242-0.0610.0760.3600.3520.063-0.1070.3230.1130.2640.3650.1740.209-0.1800.0410.0380.005-0.0500.0180.4210.0630.0000.1390.0690.0250.1700.0360.0000.1830.0000.1020.0000.0470.1050.1420.0100.0280.4030.2440.0000.0770.1790.0000.0800.0860.0000.0000.0380.1060.0000.0280.0000.1820.1380.0000.1890.0000.1550.1580.0950.1600.2010.0000.0000.0750.0180.0000.0000.0400.0400.060
BsmtFinSF1-0.015-0.1460.1620.1710.130-0.0060.1900.0630.2421.0000.052-0.5750.4080.321-0.191-0.0790.054-0.083-0.0510.2420.1800.078-0.1510.0480.0730.0580.006-0.0170.2990.0900.0210.0840.2060.1390.0000.0430.0840.1990.1070.3120.0000.1140.0950.4490.1400.1400.1670.2060.0000.1110.2150.0490.2090.2730.0690.0000.0590.1520.0440.3960.0300.1570.0150.0000.2080.0000.2970.1270.1230.1690.1740.0460.0370.1080.3600.0210.0000.0000.0730.078
BsmtFinSF2-0.006-0.0480.0820.073-0.1160.102-0.110-0.125-0.0610.0521.000-0.2720.0720.069-0.1020.001-0.0510.010-0.059-0.0060.069-0.0680.042-0.0160.0580.0680.030-0.026-0.0370.0000.0480.0000.0560.0480.1840.0000.1410.1230.0000.0380.0000.0000.1340.1530.0720.0640.0950.0390.0000.0690.0480.0000.0500.1730.4280.0000.0000.0000.0000.0890.0880.0350.0000.0000.0390.1210.0800.0540.0450.0130.0000.0000.0000.0000.1010.0820.0860.0260.0870.000
BsmtUnfSF-0.010-0.0150.0930.0750.274-0.1290.1390.1770.076-0.575-0.2721.0000.3290.2230.0620.0210.2530.1570.2610.109-0.0340.1580.0430.014-0.012-0.037-0.0440.0370.1860.0730.0000.1250.0410.0640.0000.0160.0530.1910.0150.0620.1210.1500.0920.0000.0950.1020.1680.2530.0260.1700.2080.1410.1680.2870.1490.0000.0990.0600.0190.2640.0540.1880.1210.0630.1930.0570.0880.1390.0970.1320.1800.0740.0410.0950.0000.0500.0000.0450.0920.132
TotalBsmtSF-0.036-0.2260.3720.3640.459-0.2140.4280.2990.3600.4080.0720.3291.0000.828-0.284-0.0810.3710.0610.2350.4860.2340.269-0.1750.0500.0900.047-0.0610.0280.6020.1190.0000.0620.2000.1060.0000.0280.0000.2360.0840.1450.1180.1630.1230.4240.1350.1440.2270.3170.0380.2330.3650.2650.3070.2470.2150.0730.1410.2220.0620.2040.0000.2340.0960.0720.2950.0000.3200.1830.1610.2410.2600.0670.0640.1240.3330.0700.0000.0000.1050.129
1stFlrSF-0.003-0.1370.4350.4420.408-0.1640.2940.2410.3520.3210.0690.2230.8281.000-0.273-0.0390.4960.1440.3640.4900.2220.234-0.1330.0610.1090.071-0.0330.0520.5760.1620.0000.1270.2070.0960.0000.0540.0000.2410.0890.1910.1830.1590.1490.3990.1990.1340.2140.2700.0000.0870.2230.0000.1480.0970.0000.0000.0920.1430.0190.1900.0000.2570.1240.0290.2510.0000.3400.1880.1550.2100.2370.0690.0630.1030.3400.0430.0000.0000.0880.104
2ndFlrSF0.0110.5280.0590.1230.292-0.0010.0300.0740.063-0.191-0.1020.062-0.284-0.2731.0000.0580.6440.5100.5880.0990.0700.2280.048-0.0230.0120.062-0.0050.0490.2950.1590.0000.1530.1490.0580.0000.0530.0000.2500.0520.1100.1250.4350.1200.1060.1020.1320.1120.2090.0310.1560.1770.0490.1310.1200.0170.0430.1110.0350.0000.1240.0000.4120.4490.0000.1830.0000.1650.1510.2280.1820.2280.0890.0000.1120.2950.0570.0800.0510.0000.041
LowQualFinSF-0.0280.046-0.021-0.020-0.0340.039-0.146-0.064-0.107-0.0790.0010.021-0.081-0.0390.0581.0000.0640.0210.043-0.048-0.0420.0110.0480.022-0.0190.0660.029-0.004-0.0670.1460.0000.2190.0000.0730.0000.0000.0520.1120.0000.0830.0760.2620.0000.0480.0000.0000.0000.0900.0860.0260.0350.0760.0000.0590.0000.3080.0590.1260.0000.0000.0000.0000.0000.0000.0600.0750.0000.0000.0800.1110.0870.1610.0930.0920.1340.0720.2430.0200.0000.000
GrLivArea0.0030.3490.3820.4490.604-0.1520.2890.2830.3230.054-0.0510.2530.3710.4960.6440.0641.0000.5430.8280.4690.2270.400-0.0510.0340.0870.069-0.0490.0850.7310.1060.0000.0000.2220.1000.0000.0510.0370.2090.0870.2710.0470.2590.0610.4060.1120.1220.1570.2860.0530.1520.2130.0000.1010.0960.0000.0520.1430.1580.0000.1350.0000.4680.3010.0000.2660.0000.3750.2350.2000.2480.2870.2140.1160.0940.4530.0870.0420.0420.0370.085
BedroomAbvGr0.0460.4370.3270.3390.125-0.003-0.032-0.0520.113-0.0830.0100.1570.0610.1440.5100.0210.5431.0000.6670.1160.0550.104-0.001-0.0190.0340.0720.0120.0570.2370.1660.0000.1330.0310.1110.0000.0000.1010.2060.0600.0000.3000.2420.1410.1010.0830.0690.0530.1710.0000.0860.0920.1020.1010.1030.0340.0610.0190.1610.0660.2550.0290.4470.2510.2330.1310.0280.1060.0880.1490.1230.1350.0830.0720.0980.0770.0250.0920.0250.0570.105
TotRmsAbvGrd0.0280.4320.3630.4060.430-0.1040.1790.2010.264-0.051-0.0590.2610.2350.3640.5880.0430.8280.6671.0000.3320.1640.289-0.032-0.0030.0330.060-0.0210.0450.5340.1740.0000.1250.0910.0730.0000.0000.0610.2030.0770.1270.1970.2670.1220.1300.0990.0970.1490.2750.0000.1180.1630.0700.0830.0940.0380.0370.1000.1130.0640.0660.0000.3900.2710.1740.2390.0130.2230.1750.1750.2020.2430.1100.0830.0960.1500.0560.1710.0000.0490.089
GarageArea0.0040.0510.3730.3680.540-0.2000.5280.3970.3650.242-0.0060.1090.4860.4900.099-0.0480.4690.1160.3321.0000.2500.337-0.1780.0370.0300.043-0.0350.0320.6490.1880.2590.0780.1570.1090.0000.0420.0400.2590.0650.1540.1420.1210.0770.2110.1420.1400.2450.3420.1140.1890.2920.0870.1570.1470.0100.0660.1400.2750.1020.1360.0250.2810.1600.0930.3320.0000.2280.1770.4440.6220.7580.4590.4540.2700.1940.0750.0000.0000.1320.156
WoodDeckSF-0.0420.0070.1240.1860.262-0.0440.2910.2340.1740.1800.069-0.0340.2340.2220.070-0.0420.2270.0550.1640.2501.0000.125-0.158-0.028-0.0910.0500.0170.0380.3560.0680.2220.0760.1020.1140.0000.0500.1390.1940.0580.0000.0970.0530.0550.1660.1030.1010.1350.1780.0440.1400.1750.0170.1640.1140.0620.0000.1220.1530.0400.1910.0000.2490.0820.0000.1860.0790.1450.1190.1250.1890.1480.0650.0690.0750.1160.0760.0350.0290.0000.030
OpenPorchSF-0.0070.0410.1680.1780.433-0.1320.3900.3510.2090.078-0.0680.1580.2690.2340.2280.0110.4000.1040.2890.3370.1251.000-0.1670.0180.0080.037-0.0340.0630.4770.1460.0390.0820.0740.0000.0450.0000.0000.1200.0610.2800.0000.1270.0150.1100.0780.0810.1060.1800.1610.1240.1330.0000.0330.0720.0230.0740.1030.1010.0000.0780.0000.1680.1480.0000.1590.0850.1230.0600.0900.1460.1290.0850.0000.0340.1010.0400.0000.0000.0470.067
EnclosedPorch-0.0050.104-0.075-0.070-0.1610.113-0.408-0.233-0.180-0.1510.0420.043-0.175-0.1330.0480.048-0.051-0.001-0.032-0.178-0.158-0.1671.000-0.039-0.0810.0040.039-0.026-0.2190.1470.0000.2090.0600.0270.0000.0610.0000.1440.0460.0460.0340.1030.1720.0000.1940.1890.0970.0880.0350.2480.1390.0960.0540.0840.0650.1140.1270.2290.0540.0280.0480.1070.0770.0260.0910.0310.0440.1030.0890.1210.2470.1030.1110.1940.4030.0690.0000.0250.0320.057
3SsnPorch-0.037-0.0140.0760.0630.0340.0320.0230.0530.0410.048-0.0160.0140.0500.061-0.0230.0220.034-0.019-0.0030.037-0.0280.018-0.0391.000-0.038-0.0090.0050.0370.0660.0000.0000.0000.0160.0730.0000.0480.0640.0000.1330.0000.0000.0000.0000.1740.0000.0000.0000.0310.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0510.0000.0000.0000.0180.0000.0000.0000.000
ScreenPorch0.006-0.0120.0370.0930.0480.074-0.072-0.0440.0380.0730.058-0.0120.0900.1090.012-0.0190.0870.0340.0330.030-0.0910.008-0.081-0.0381.0000.0190.0150.0240.1020.0000.0750.0470.0380.0000.2140.0300.0720.0450.0080.0000.0000.0700.0650.0880.0390.0740.0000.0050.0000.0000.0000.0000.0060.0450.0350.0300.0000.0000.0000.0000.0000.0480.0470.0000.0250.0500.1250.0590.0180.0080.0000.1480.0000.0000.2780.0000.3490.0000.0000.000
PoolArea0.0560.0660.0860.0850.057-0.0060.0090.0030.0050.0580.068-0.0370.0470.0710.0620.0660.0690.0720.0600.0430.0500.0370.004-0.0090.0191.0000.041-0.0230.0590.0000.0000.0000.1210.0000.0000.0390.0000.0000.0360.0000.0000.0370.1290.3780.1270.1460.0000.0320.0000.0000.0000.0000.0100.0000.0720.0000.0580.0000.0000.0990.0000.1080.0000.0000.0150.0000.1860.1200.0000.0000.0000.1200.1560.0000.7500.1020.2840.0000.0000.147
MiscVal-0.0430.0290.0400.060-0.0870.086-0.091-0.090-0.0500.0060.030-0.044-0.061-0.033-0.0050.029-0.0490.012-0.021-0.0350.017-0.0340.0390.0050.0150.0411.0000.011-0.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4960.1000.0000.3510.0000.0000.0000.0440.1490.0920.1250.0670.0660.0780.0740.0910.0000.0560.0860.0000.0000.0000.0000.1980.0850.0790.1170.0510.0280.0840.0550.0320.0000.0000.0890.1740.0160.6630.0210.0000.000
MoSold0.0170.0770.0170.0070.060-0.0070.0150.0170.018-0.017-0.0260.0370.0280.0520.049-0.0040.0850.0570.0450.0320.0380.063-0.0260.0370.024-0.0230.0111.0000.0690.0260.0570.0290.0000.0800.0470.0320.0460.0520.0000.0000.0070.0000.0000.0000.0000.0000.0110.0320.0000.0000.0000.0000.0000.0120.0120.0000.0170.0000.0000.0000.0000.0450.0570.0250.0310.0260.0000.0070.0190.0090.0000.0000.0240.0000.0000.0230.0000.1540.0260.044
SalePrice-0.0200.0890.4030.4580.809-0.1270.6520.5700.4210.299-0.0370.1860.6020.5760.295-0.0670.7310.2370.5340.6490.3560.477-0.2190.0660.1020.059-0.0620.0691.0000.2050.0000.1020.2000.0960.0000.0880.0460.3170.0650.0000.0870.1280.1140.1210.1650.1750.2460.4710.1040.2560.4070.1560.2120.2110.1070.0810.2380.4180.1150.1400.0480.4180.2070.0500.4590.0390.2900.2710.2490.3900.4150.2230.1840.2210.2820.1110.0000.0000.1260.166
MSZoning0.0000.3390.2060.0000.1880.1610.2930.1990.0630.0900.0000.0730.1190.1620.1590.1460.1060.1660.1740.1880.0680.1460.1470.0000.0000.0000.0000.0260.2051.0000.2490.3880.1530.1020.0000.0650.0710.6390.0710.0580.1880.1850.0720.0000.1760.1840.0990.2370.0780.2220.1630.0790.0640.1330.0370.0540.1150.2960.0990.0700.0190.1750.1390.0920.1720.0000.1350.1180.2110.1930.1430.1250.1010.2180.0000.0230.0000.0000.1470.132
Street0.0000.0910.3000.2910.0720.0680.0000.1100.0000.0210.0480.0000.0000.0000.0000.0000.0000.0000.0000.2590.2220.0390.0000.0000.0750.0000.0000.0570.0000.2491.0000.0000.0340.1140.0000.0000.1760.1980.1650.0000.1120.0190.0000.0000.0000.0000.0000.3210.0000.0440.0000.0000.0870.0000.1040.0000.0170.0400.0000.0910.0000.0210.0000.0000.0610.0000.0600.0000.2230.0000.0270.0000.0000.0000.0000.0000.1580.0350.1110.106
Alley0.0000.3060.1240.0000.0990.1170.3120.1410.1390.0840.0000.1250.0620.1270.1530.2190.0000.1330.1250.0780.0760.0820.2090.0000.0470.0000.0000.0290.1020.3880.0001.0000.0830.0720.0000.0340.0000.4270.1260.0000.1480.1440.1080.0000.2080.2020.0930.1080.0420.2220.1300.0760.0820.1140.0000.1230.0870.2010.1280.0650.0000.0710.0410.0000.0920.0340.0730.0710.2000.1410.0830.1230.1410.1790.0000.0000.0000.0000.0260.060
LotShape0.0090.1660.2720.2660.1180.0630.1750.1400.0690.2060.0560.0410.2000.2070.1490.0000.2220.0310.0910.1570.1020.0740.0600.0160.0380.1210.0000.0000.2000.1530.0340.0831.0000.1260.0000.2210.1190.2460.1050.0000.0860.0740.0350.1860.0820.0930.0680.1140.0000.1180.1430.0600.1060.0650.0650.0250.0550.1080.1100.0940.0450.1040.0860.0410.0950.0000.1410.1230.1440.1430.1210.0760.0520.0760.0940.0310.0000.0000.0000.019
LandContour0.0000.1400.1910.2580.1610.1010.1590.1290.0250.1390.0480.0640.1060.0960.0580.0730.1000.1110.0730.1090.1140.0000.0270.0730.0000.0000.0000.0800.0960.1020.1140.0720.1261.0000.0000.0610.4570.3600.0000.0590.0690.1260.1400.1800.1160.1210.0880.1340.0000.1000.0930.0610.1930.0870.0000.0000.0530.1280.0440.1060.0260.1110.0000.0000.0970.0000.0670.0760.1160.1020.0900.0370.0000.1160.0000.0320.0000.0000.0310.112
Utilities0.0100.0000.0000.0000.0000.0000.0000.0790.1700.0000.1840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.2140.0000.0000.0470.0000.0000.0000.0000.0000.0001.0000.0860.0000.0950.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0270.0000.0810.0000.1020.0000.0000.0000.0000.0000.0000.0000.2190.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.076
LotConfig0.0000.0790.1270.0720.0210.0000.1050.0860.0360.0430.0000.0160.0280.0540.0530.0000.0510.0000.0000.0420.0500.0000.0610.0480.0300.0390.0000.0320.0880.0650.0000.0340.2210.0610.0861.0000.0800.1390.1480.0910.0690.0000.0740.0780.0550.0750.0000.0130.0000.0430.0720.0090.0540.0540.0000.0000.0140.0630.0000.0000.0340.0380.0160.0460.0000.0000.0470.0470.0570.0320.0450.0150.0520.0290.0230.0000.0000.0340.0000.030
LandSlope0.0250.0860.3680.4510.1520.1880.0970.0820.0000.0840.1410.0530.0000.0000.0000.0520.0370.1010.0610.0400.1390.0000.0000.0640.0720.0000.0000.0460.0460.0710.1760.0000.1190.4570.0000.0801.0000.3160.0000.0000.0260.0000.2560.3130.1330.1160.0470.0930.0000.0490.0000.1330.2220.0420.0750.0000.0490.0000.0000.2000.0450.1240.0410.0000.0450.0760.1560.0320.1090.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.040
Neighborhood0.0000.3750.2240.1640.3200.2230.4810.3870.1830.1990.1230.1910.2360.2410.2500.1120.2090.2060.2030.2590.1940.1200.1440.0000.0450.0000.0000.0520.3170.6390.1980.4270.2460.3600.0950.1390.3161.0000.1850.0030.4190.2940.1860.0950.2890.3170.3830.4820.1530.4170.4640.1270.2430.2890.1530.0530.2960.3820.1660.1900.1440.3700.3000.1020.4420.0810.3050.3040.2980.4170.3900.1980.1720.3090.0500.1750.0000.0000.1670.216
Condition10.0000.1110.0800.0000.0620.0510.1190.0790.0000.1070.0000.0150.0840.0890.0520.0000.0870.0600.0770.0650.0580.0610.0460.1330.0080.0360.0000.0000.0650.0710.1650.1260.1050.0000.0000.1480.0000.1851.0000.2100.0760.0850.0810.0770.0720.0790.0470.1240.0180.0790.1310.0390.0640.0460.0230.0000.1570.0390.0130.0000.0000.0610.0870.0690.0820.0000.0480.0000.0890.1210.0620.0280.0300.1030.0690.0300.0750.0000.0330.000
Condition20.0000.1570.0420.0000.1570.0990.1620.0000.1020.3120.0380.0620.1450.1910.1100.0830.2710.0000.1270.1540.0000.2800.0460.0000.0000.0000.4960.0000.0000.0580.0000.0000.0000.0590.0000.0910.0000.0030.2101.0000.1440.1220.3110.0000.0250.0000.0000.1380.2840.0350.0890.0000.0000.0000.0000.0000.0690.0680.0000.0000.0000.1080.1990.1240.0900.0000.0000.0000.0950.0000.0000.1430.0000.0570.0000.0000.3480.0000.0000.000
BldgType0.0000.8910.3310.0360.1300.1270.2500.1960.0000.0000.0000.1210.1180.1830.1250.0760.0470.3000.1970.1420.0970.0000.0340.0000.0000.0000.1000.0070.0870.1880.1120.1480.0860.0690.0000.0690.0260.4190.0760.1441.0000.1560.0480.0270.1640.1910.0900.1750.1100.1880.2040.1530.1590.1780.1490.1070.1120.2880.0760.1600.0650.1060.2300.4920.1490.0570.1250.0960.1770.1880.1540.1220.1090.1470.0000.0630.0660.0000.0970.158
HouseStyle0.0180.8480.0370.0000.1450.1220.2900.2000.0470.1140.0000.1500.1630.1590.4350.2620.2590.2420.2670.1210.0530.1270.1030.0000.0700.0370.0000.0000.1280.1850.0190.1440.0740.1260.0990.0000.0000.2940.0850.1220.1561.0000.1020.0470.1600.1670.1640.1750.1030.2160.1840.0780.1990.1480.0560.1410.1680.2330.1070.1650.0980.2370.4610.1500.1460.0350.0990.1090.2000.2040.1640.1430.1290.1630.0530.1090.0000.0000.0540.087
RoofStyle0.0500.2170.1110.1140.1190.0440.1600.0810.1050.0950.1340.0920.1230.1490.1200.0000.0610.1410.1220.0770.0550.0150.1720.0000.0650.1290.3510.0000.1140.0720.0000.1080.0350.1400.0000.0740.2560.1860.0810.3110.0480.1021.0000.4580.1380.1600.1300.1490.0920.0920.1410.0400.1140.0500.0700.0000.0000.0550.0000.1250.1260.1370.2100.1610.1120.1250.0790.0840.0680.0930.1340.0000.0450.1170.0740.0070.2460.0000.0000.089
RoofMatl0.0000.0800.2780.2530.1030.0000.0700.0400.1420.4490.1530.0000.4240.3990.1060.0480.4060.1010.1300.2110.1660.1100.0000.1740.0880.3780.0000.0000.1210.0000.0000.0000.1860.1800.0000.0780.3130.0950.0770.0000.0270.0470.4581.0000.1860.1150.0510.0670.0000.0000.0210.0230.1230.0370.0980.0000.0000.0000.0000.1660.1390.1060.0190.1750.0390.1530.2700.0510.0000.0000.0000.0860.0560.0000.3410.0000.0000.0000.0000.058
Exterior1st0.0000.1930.0930.0210.2010.1900.3340.2840.0100.1400.0720.0950.1350.1990.1020.0000.1120.0830.0990.1420.1030.0780.1940.0000.0390.1270.0000.0000.1650.1760.0000.2080.0820.1160.0000.0550.1330.2890.0720.0250.1640.1600.1380.1861.0000.7600.2250.3520.0940.3140.2970.1350.1510.2120.1460.1340.2640.3490.1780.0910.0650.2370.1180.1580.2900.0930.1470.1750.1930.2790.2440.1150.1270.1900.0850.1260.0000.0420.1180.174
Exterior2nd0.0000.2040.1030.0620.1950.1670.3260.2770.0280.1400.0640.1020.1440.1340.1320.0000.1220.0690.0970.1400.1010.0810.1890.0000.0740.1460.0000.0000.1750.1840.0000.2020.0930.1210.0000.0750.1160.3170.0790.0000.1910.1670.1600.1150.7601.0000.2160.3560.0660.3140.2850.1120.1520.2130.1250.1810.2650.3330.1600.0860.0670.2210.1640.1150.2850.0730.1190.1390.1940.2720.2350.0970.1000.1740.1440.1070.0000.0360.1160.154
MasVnrType0.0000.2300.1800.1030.2810.1680.3080.2810.4030.1670.0950.1680.2270.2140.1120.0000.1570.0530.1490.2450.1350.1060.0970.0000.0000.0000.0440.0110.2460.0990.0000.0930.0680.0880.0000.0000.0470.3830.0470.0000.0900.1640.1300.0510.2250.2161.0000.2490.0640.2100.2540.0600.1260.2010.0720.0000.1540.1790.0790.0850.0520.1750.1000.0070.2240.1690.1310.2010.2320.2220.2600.1220.1100.1480.0000.0770.0290.0240.1890.199
ExterQual0.0000.2840.1200.0000.6120.3200.4340.3880.2440.2060.0390.2530.3170.2700.2090.0900.2860.1710.2750.3420.1780.1800.0880.0310.0050.0320.1490.0320.4710.2370.3210.1080.1140.1340.0000.0130.0930.4820.1240.1380.1750.1750.1490.0670.3520.3560.2491.0000.1810.3690.4620.1670.1670.2970.1120.0420.3230.2780.1360.0680.0410.3180.1490.0910.5440.1030.1880.2410.2880.3370.3590.1500.1530.1920.0310.1540.1110.0440.2580.234
ExterCond0.0320.1670.0000.0000.1950.3800.1880.0980.0000.0000.0000.0260.0380.0000.0310.0860.0530.0000.0000.1140.0440.1610.0350.0000.0000.0000.0920.0000.1040.0780.0000.0420.0000.0000.0000.0000.0000.1530.0180.2840.1100.1030.0920.0000.0940.0660.0640.1811.0000.1220.1040.1870.0420.0760.0070.0460.0620.2000.1260.0000.0590.0750.0510.0000.1770.1620.0350.0110.1130.1430.1290.1640.1290.1540.0000.0830.0850.0120.0900.052
Foundation0.0000.3590.0820.0000.2900.2560.5030.3220.0770.1110.0690.1700.2330.0870.1560.0260.1520.0860.1180.1890.1400.1240.2480.2500.0000.0000.1250.0000.2560.2220.0440.2220.1180.1000.0000.0430.0490.4170.0790.0350.1880.2160.0920.0000.3140.3140.2100.3690.1221.0000.5310.4170.4100.4540.3690.2160.2920.3650.1610.1020.0660.2840.1620.1690.3420.1010.1220.1150.2340.3060.2690.1920.1360.2360.0000.1350.0840.0330.1490.159
BsmtQual0.0000.3340.1000.0000.4570.2830.4560.3420.1790.2150.0480.2080.3650.2230.1770.0350.2130.0920.1630.2920.1750.1330.1390.0000.0000.0000.0670.0000.4070.1630.0000.1300.1430.0930.0000.0720.0000.4640.1310.0890.2040.1840.1410.0210.2970.2850.2540.4620.1040.5311.0000.5260.5210.5760.5000.1790.2400.2830.1980.1240.0530.3420.1730.1860.4200.1130.1820.2420.2420.3400.3480.1640.1510.1940.0000.1330.0530.0000.2100.226
BsmtCond0.0000.2000.0000.0000.3080.4160.1920.1210.0000.0490.0000.1410.2650.0000.0490.0760.0000.1020.0700.0870.0170.0000.0960.0000.0000.0000.0660.0000.1560.0790.0000.0760.0600.0610.0000.0090.1330.1270.0390.0000.1530.0780.0400.0230.1350.1120.0600.1670.1870.4170.5261.0000.4950.5070.4950.1870.1020.3150.3770.1060.0600.1420.0940.1850.1370.2040.0500.0440.1130.1230.0970.2140.1770.1690.0000.0000.0240.0430.0700.095
BsmtExposure0.0000.2620.1240.1250.2080.1040.1900.1500.0800.2090.0500.1680.3070.1480.1310.0000.1010.1010.0830.1570.1640.0330.0540.0000.0060.0100.0780.0000.2120.0640.0870.0820.1060.1930.0000.0540.2220.2430.0640.0000.1590.1990.1140.1230.1510.1520.1260.1670.0420.4100.5210.4951.0000.5220.4910.1740.0960.2110.1060.2130.0470.0970.1060.1750.1500.0630.1380.0870.1390.1590.1430.0610.0650.1120.0110.0000.0410.0270.0930.116
BsmtFinType10.0000.2320.0530.0000.2400.1620.3290.2440.0860.2730.1730.2870.2470.0970.1200.0590.0960.1030.0940.1470.1140.0720.0840.0000.0450.0000.0740.0120.2110.1330.0000.1140.0650.0870.0000.0540.0420.2890.0460.0000.1780.1480.0500.0370.2120.2130.2010.2970.0760.4540.5760.5070.5221.0000.4460.1640.2090.2590.1210.3420.0820.2220.0980.1760.2810.0970.1200.1240.1560.2220.2150.0830.0810.1920.0000.1150.0690.0000.0930.121
BsmtFinType20.0000.1420.0610.0570.1360.0750.1500.1200.0000.0690.4280.1490.2150.0000.0170.0000.0000.0340.0380.0100.0620.0230.0650.0000.0350.0720.0910.0120.1070.0370.1040.0000.0650.0000.1170.0000.0750.1530.0230.0000.1490.0560.0700.0980.1460.1250.0720.1120.0070.3690.5000.4950.4910.4461.0000.1500.1010.1980.0710.1030.0910.0400.0920.1640.0880.0940.0570.0490.0770.0850.0630.0450.0490.0940.1160.0880.0850.0000.0540.068
Heating0.0000.1500.0000.0840.1670.0920.1710.0830.0000.0000.0000.0000.0730.0000.0430.3080.0520.0610.0370.0660.0000.0740.1140.0000.0300.0000.0000.0000.0810.0540.0000.1230.0250.0000.0000.0000.0000.0530.0000.0000.1070.1410.0000.0000.1340.1810.0000.0420.0460.2160.1790.1870.1740.1640.1501.0000.2380.4610.1150.0000.0000.0000.0000.0900.1550.0620.0000.0000.0950.1090.0890.1270.1680.1470.0000.0000.0000.0280.0640.000
HeatingQC0.0000.2380.0230.0000.2580.1770.3350.3260.0380.0590.0000.0990.1410.0920.1110.0590.1430.0190.1000.1400.1220.1030.1270.0000.0000.0580.0560.0170.2380.1150.0170.0870.0550.0530.0270.0140.0490.2960.1570.0690.1120.1680.0000.0000.2640.2650.1540.3230.0620.2920.2400.1020.0960.2090.1010.2381.0000.3780.1450.0590.0280.1990.0940.0940.3170.0230.0980.1170.1580.2400.1800.0930.0960.1740.0390.1050.0320.0030.1300.147
CentralAir0.0000.4340.0370.0000.3740.3170.4380.3770.1060.1520.0000.0600.2220.1430.0350.1260.1580.1610.1130.2750.1530.1010.2290.0000.0000.0000.0860.0000.4180.2960.0400.2010.1080.1280.0000.0630.0000.3820.0390.0680.2880.2330.0550.0000.3490.3330.1790.2780.2000.3650.2830.3150.2110.2590.1980.4610.3781.0000.4200.1050.0170.1020.1300.2470.3420.0890.1970.1960.3600.3250.2820.3190.3520.3350.0000.0000.0410.0000.1270.112
Electrical0.0340.1610.0000.0000.1400.2400.1650.1950.0000.0440.0000.0190.0620.0190.0000.0000.0000.0660.0640.1020.0400.0000.0540.0000.0000.0000.0000.0000.1150.0990.0000.1280.1100.0440.0810.0000.0000.1660.0130.0000.0760.1070.0000.0000.1780.1600.0790.1360.1260.1610.1980.3770.1060.1210.0710.1150.1450.4201.0000.0490.0000.1130.0810.1050.2000.1950.0830.0520.1150.1470.1240.3060.2100.1910.0000.0000.0000.0000.0000.140
BsmtFullBath0.0000.2390.1640.2110.0650.0000.1440.1220.0280.3960.0890.2640.2040.1900.1240.0000.1350.2550.0660.1360.1910.0780.0280.0000.0000.0990.0000.0000.1400.0700.0910.0650.0940.1060.0000.0000.2000.1900.0000.0000.1600.1650.1250.1660.0910.0860.0850.0680.0000.1020.1240.1060.2130.3420.1030.0000.0590.1050.0491.0000.0960.2650.1520.1320.0940.0000.1110.0550.1280.1130.1150.0810.0750.0850.0800.0000.0000.0530.1150.218
BsmtHalfBath0.0000.0840.0090.0000.0620.1010.0880.0760.0000.0300.0880.0540.0000.0000.0000.0000.0000.0290.0000.0250.0000.0000.0480.0570.0000.0000.0000.0000.0480.0190.0000.0000.0450.0260.1020.0340.0450.1440.0000.0000.0650.0980.1260.1390.0650.0670.0520.0410.0590.0660.0530.0600.0470.0820.0910.0000.0280.0170.0000.0961.0000.1640.1540.4980.0000.0000.0000.0340.0250.0610.0720.0330.0280.0170.0260.0370.0000.0240.0060.254
FullBath0.0000.3140.1210.0900.4070.3100.3510.2700.1820.1570.0350.1880.2340.2570.4120.0000.4680.4470.3900.2810.2490.1680.1070.0000.0480.1080.0000.0450.4180.1750.0210.0710.1040.1110.0000.0380.1240.3700.0610.1080.1060.2370.1370.1060.2370.2210.1750.3180.0750.2840.3420.1420.0970.2220.0400.0000.1990.1020.1130.2650.1641.0000.2310.1140.2780.0550.1780.1920.2660.2660.3290.0900.0870.0980.0900.1390.0950.0000.1350.182
HalfBath0.0000.5340.0000.0000.2240.0770.2260.2000.1380.0150.0000.1210.0960.1240.4490.0000.3010.2510.2710.1600.0820.1480.0770.0000.0470.0000.1980.0570.2070.1390.0000.0410.0860.0000.0000.0160.0410.3000.0870.1990.2300.4610.2100.0190.1180.1640.1000.1490.0510.1620.1730.0940.1060.0980.0920.0000.0940.1300.0810.1520.1540.2311.0000.1930.1460.0330.1640.1720.2330.1970.1960.1050.1130.0850.0000.0770.1400.0000.0330.133
KitchenAbvGr0.0400.4980.0000.0000.1070.0720.2140.1110.0000.0000.0000.0630.0720.0290.0000.0000.0000.2330.1740.0930.0000.0000.0260.0000.0000.0000.0850.0250.0500.0920.0000.0000.0410.0000.0000.0460.0000.1020.0690.1240.4920.1500.1610.1750.1580.1150.0070.0910.0000.1690.1860.1850.1750.1760.1640.0900.0940.2470.1050.1320.4980.1140.1931.0000.1010.0000.0870.0790.1750.1420.1240.1280.1890.1350.0000.0000.0430.0000.0000.316
KitchenQual0.0000.2820.0920.0000.5380.2460.4010.4160.1890.2080.0390.1930.2950.2510.1830.0600.2660.1310.2390.3320.1860.1590.0910.0000.0250.0150.0790.0310.4590.1720.0610.0920.0950.0970.0000.0000.0450.4420.0820.0900.1490.1460.1120.0390.2900.2850.2240.5440.1770.3420.4200.1370.1500.2810.0880.1550.3170.3420.2000.0940.0000.2780.1460.1011.0000.0870.1850.2580.2650.3190.3620.1870.1860.1910.0300.1150.0730.0000.2070.209
Functional0.0380.0990.0000.0000.1180.1660.0850.0510.0000.0000.1210.0570.0000.0000.0000.0750.0000.0280.0130.0000.0790.0850.0310.0000.0500.0000.1170.0260.0390.0000.0000.0340.0000.0000.0000.0000.0760.0810.0000.0000.0570.0350.1250.1530.0930.0730.1690.1030.1620.1010.1130.2040.0630.0970.0940.0620.0230.0890.1950.0000.0000.0550.0330.0000.0871.0000.0000.0950.1430.0780.0430.1120.0670.0740.0000.0530.0900.0480.0240.000
Fireplaces0.0000.2220.2480.1570.2680.1040.1700.1370.1550.2970.0800.0880.3200.3400.1650.0000.3750.1060.2230.2280.1450.1230.0440.0000.1250.1860.0510.0000.2900.1350.0600.0730.1410.0670.0000.0470.1560.3050.0480.0000.1250.0990.0790.2700.1470.1190.1310.1880.0350.1220.1820.0500.1380.1200.0570.0000.0980.1970.0830.1110.0000.1780.1640.0870.1850.0001.0000.5800.2340.2290.2030.1270.1190.1080.1510.0470.0370.0270.0790.082
FireplaceQu0.0000.1890.1020.0000.2690.0870.2130.2250.1580.1270.0540.1390.1830.1880.1510.0000.2350.0880.1750.1770.1190.0600.1030.0000.0590.1200.0280.0070.2710.1180.0000.0710.1230.0760.0000.0470.0320.3040.0000.0000.0960.1090.0840.0510.1750.1390.2010.2410.0110.1150.2420.0440.0870.1240.0490.0000.1170.1960.0520.0550.0340.1920.1720.0790.2580.0950.5801.0000.1820.2350.2250.0960.0950.1210.1220.0710.0100.0000.1260.122
GarageType0.0000.3310.1010.0450.2240.1670.2650.1880.0950.1230.0450.0970.1610.1550.2280.0800.2000.1490.1750.4440.1250.0900.0890.0000.0180.0000.0840.0190.2490.2110.2230.2000.1440.1160.2190.0570.1090.2980.0890.0950.1770.2000.0680.0000.1930.1940.2320.2880.1130.2340.2420.1130.1390.1560.0770.0950.1580.3600.1150.1280.0250.2660.2330.1750.2650.1430.2340.1821.0000.6840.5370.4590.4600.2880.0000.0750.0300.0000.1000.141
GarageFinish0.0000.3820.1190.0340.3720.2300.3880.2860.1600.1690.0130.1320.2410.2100.1820.1110.2480.1230.2020.6220.1890.1460.1210.0240.0080.0000.0550.0090.3900.1930.0000.1410.1430.1020.0000.0320.0000.4170.1210.0000.1880.2040.0930.0000.2790.2720.2220.3370.1430.3060.3400.1230.1590.2220.0850.1090.2400.3250.1470.1130.0610.2660.1970.1420.3190.0780.2290.2350.6841.0000.6350.5890.5860.2690.0120.0950.0360.0000.1680.185
GarageCars0.0000.2950.1720.0130.4010.2190.3400.2760.2010.1740.0000.1800.2600.2370.2280.0870.2870.1350.2430.7580.1480.1290.2470.0000.0000.0000.0320.0000.4150.1430.0270.0830.1210.0900.0000.0450.0340.3900.0620.0000.1540.1640.1340.0000.2440.2350.2600.3590.1290.2690.3480.0970.1430.2150.0630.0890.1800.2820.1240.1150.0720.3290.1960.1240.3620.0430.2030.2250.5370.6351.0000.5080.5050.2620.0000.1140.0180.0000.1910.213
GarageQual0.0000.2350.0000.0000.1600.1630.2330.1090.0000.0460.0000.0740.0670.0690.0890.1610.2140.0830.1100.4590.0650.0850.1030.0510.1480.1200.0000.0000.2230.1250.0000.1230.0760.0370.0000.0150.0000.1980.0280.1430.1220.1430.0000.0860.1150.0970.1220.1500.1640.1920.1640.2140.0610.0830.0450.1270.0930.3190.3060.0810.0330.0900.1050.1280.1870.1120.1270.0960.4590.5890.5081.0000.7040.2810.0650.0000.0100.0270.0230.085
GarageCond0.0000.2120.0000.0000.1640.1360.1980.1080.0000.0370.0000.0410.0640.0630.0000.0930.1160.0720.0830.4540.0690.0000.1110.0000.0000.1560.0000.0240.1840.1010.0000.1410.0520.0000.0000.0520.0000.1720.0300.0000.1090.1290.0450.0560.1270.1000.1100.1530.1290.1360.1510.1770.0650.0810.0490.1680.0960.3520.2100.0750.0280.0870.1130.1890.1860.0670.1190.0950.4600.5860.5050.7041.0000.3020.0930.0840.0210.0000.0420.087
PavedDrive0.0380.3090.0510.0340.1750.1880.3440.1750.0750.1080.0000.0950.1240.1030.1120.0920.0940.0980.0960.2700.0750.0340.1940.0000.0000.0000.0890.0000.2210.2180.0000.1790.0760.1160.0000.0290.0000.3090.1030.0570.1470.1630.1170.0000.1900.1740.1480.1920.1540.2360.1940.1690.1120.1920.0940.1470.1740.3350.1910.0850.0170.0980.0850.1350.1910.0740.1080.1210.2880.2690.2620.2810.3021.0000.0000.0000.0440.0000.0690.105
PoolQC0.0250.0930.3250.1280.1090.0000.0000.0190.0180.3600.1010.0000.3330.3400.2950.1340.4530.0770.1500.1940.1160.1010.4030.0000.2780.7500.1740.0000.2820.0000.0000.0000.0940.0000.0000.0230.0000.0500.0690.0000.0000.0530.0740.3410.0850.1440.0000.0310.0000.0000.0000.0000.0110.0000.1160.0000.0390.0000.0000.0800.0260.0900.0000.0000.0300.0000.1510.1220.0000.0120.0000.0650.0930.0001.0000.1050.4050.0250.0000.115
Fence0.0000.1310.0000.0000.1310.1160.1700.1400.0000.0210.0820.0500.0700.0430.0570.0720.0870.0250.0560.0750.0760.0400.0690.0180.0000.1020.0160.0230.1110.0230.0000.0000.0310.0320.0000.0000.0000.1750.0300.0000.0630.1090.0070.0000.1260.1070.0770.1540.0830.1350.1330.0000.0000.1150.0880.0000.1050.0000.0000.0000.0370.1390.0770.0000.1150.0530.0470.0710.0750.0950.1140.0000.0840.0000.1051.0000.0560.0000.0550.071
MiscFeature0.0270.1790.0860.0770.0000.0160.0460.0620.0000.0000.0860.0000.0000.0000.0800.2430.0420.0920.1710.0000.0350.0000.0000.0000.3490.2840.6630.0000.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.0750.3480.0660.0000.2460.0000.0000.0000.0290.1110.0850.0840.0530.0240.0410.0690.0850.0000.0320.0410.0000.0000.0000.0950.1400.0430.0730.0900.0370.0100.0300.0360.0180.0100.0210.0440.4050.0561.0000.0400.0000.000
YrSold0.0080.0000.0130.0000.0000.0510.0000.0000.0400.0000.0260.0450.0000.0000.0510.0200.0420.0250.0000.0000.0290.0000.0250.0000.0000.0000.0210.1540.0000.0000.0350.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0420.0360.0240.0440.0120.0330.0000.0430.0270.0000.0000.0280.0030.0000.0000.0530.0240.0000.0000.0000.0000.0480.0270.0000.0000.0000.0000.0270.0000.0000.0250.0000.0401.0000.0840.080
SaleType0.0000.0900.0000.0000.1610.1040.1560.2060.0400.0730.0870.0920.1050.0880.0000.0000.0370.0570.0490.1320.0000.0470.0320.0000.0000.0000.0000.0260.1260.1470.1110.0260.0000.0310.1310.0000.0000.1670.0330.0000.0970.0540.0000.0000.1180.1160.1890.2580.0900.1490.2100.0700.0930.0930.0540.0640.1300.1270.0000.1150.0060.1350.0330.0000.2070.0240.0790.1260.1000.1680.1910.0230.0420.0690.0000.0550.0000.0841.0000.471
SaleCondition0.0000.1560.0450.0000.1510.1140.1970.2560.0600.0780.0000.1320.1290.1040.0410.0000.0850.1050.0890.1560.0300.0670.0570.0000.0000.1470.0000.0440.1660.1320.1060.0600.0190.1120.0760.0300.0400.2160.0000.0000.1580.0870.0890.0580.1740.1540.1990.2340.0520.1590.2260.0950.1160.1210.0680.0000.1470.1120.1400.2180.2540.1820.1330.3160.2090.0000.0820.1220.1410.1850.2130.0850.0870.1050.1150.0710.0000.0800.4711.000

Missing values

2023-01-10T04:43:00.808293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-10T04:43:03.221876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.0000008450PaveNoInfoRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NoInfoAttchd2003.0RFn2548TATAY0610000NoInfoNoInfoNoInfo022008WDNormal208500
1220RL80.0000009600PaveNoInfoRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NoInfoNoInfoNoInfo052007WDNormal181500
2360RL68.00000011250PaveNoInfoIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NoInfoNoInfoNoInfo092008WDNormal223500
3470RL60.0000009550PaveNoInfoIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NoInfoNoInfoNoInfo022006WDAbnorml140000
4560RL84.00000014260PaveNoInfoIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NoInfoNoInfoNoInfo0122008WDNormal250000
5650RL85.00000014115PaveNoInfoIR1LvlAllPubInsideGtlMitchelNormNorm1Fam1.5Fin5519931995GableCompShgVinylSdVinylSdNone0.0TATAWoodGdTANoGLQ732Unf064796GasAExYSBrkr79656601362101111TA5Typ0NoInfoAttchd1993.0Unf2480TATAY4030032000NoInfoMnPrvShed700102009WDNormal143000
6720RL75.00000010084PaveNoInfoRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520042005GableCompShgVinylSdVinylSdStone186.0GdTAPConcExTAAvGLQ1369Unf03171686GasAExYSBrkr1694001694102031Gd7Typ1GdAttchd2004.0RFn2636TATAY255570000NoInfoNoInfoNoInfo082007WDNormal307000
7860RL69.78182410382PaveNoInfoIR1LvlAllPubCornerGtlNWAmesPosNNorm1Fam2Story7619731973GableCompShgHdBoardHdBoardStone240.0TATACBlockGdTAMnALQ859BLQ322161107GasAExYSBrkr110798302090102131TA7Typ2TAAttchd1973.0RFn2484TATAY235204228000NoInfoNoInfoShed350112009WDNormal200000
8950RM51.0000006120PaveNoInfoRegLvlAllPubInsideGtlOldTownArteryNorm1Fam1.5Fin7519311950GableCompShgBrkFaceWd ShngNone0.0TATABrkTilTATANoUnf0Unf0952952GasAGdYFuseF102275201774002022TA8Min12TADetchd1931.0Unf2468FaTAY900205000NoInfoNoInfoNoInfo042008WDAbnorml129900
910190RL50.0000007420PaveNoInfoRegLvlAllPubCornerGtlBrkSideArteryArtery2fmCon1.5Unf5619391950GableCompShgMetalSdMetalSdNone0.0TATABrkTilTATANoGLQ851Unf0140991GasAExYSBrkr1077001077101022TA5Typ2TAAttchd1939.0RFn1205GdTAY040000NoInfoNoInfoNoInfo012008WDNormal118000
IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
1450145190RL60.09000PaveNoInfoRegLvlAllPubFR2GtlNAmesNormNormDuplex2Story5519741974GableCompShgVinylSdVinylSdNone0.0TATACBlockGdTANoUnf0Unf0896896GasATAYSBrkr89689601792002242TA8Typ0NoInfoNoInfoYearBuiltNoInfo00NoInfoNoInfoY32450000NoInfoNoInfoNoInfo092009WDNormal136000
1451145220RL78.09262PaveNoInfoRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520082009GableCompShgCemntBdCmentBdStone194.0GdTAPConcGdTANoUnf0Unf015731573GasAExYSBrkr1578001578002031Ex7Typ1GdAttchd2008.0Fin3840TATAY0360000NoInfoNoInfoNoInfo052009NewPartial287090
14521453180RM35.03675PaveNoInfoRegLvlAllPubInsideGtlEdwardsNormNormTwnhsESLvl5520052005GableCompShgVinylSdVinylSdBrkFace80.0TATAPConcGdTAGdGLQ547Unf00547GasAGdYSBrkr1072001072101021TA5Typ0NoInfoBasment2005.0Fin2525TATAY0280000NoInfoNoInfoNoInfo052006WDNormal145000
1453145420RL90.017217PaveNoInfoRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story5520062006GableCompShgVinylSdVinylSdNone0.0TATAPConcGdTANoUnf0Unf011401140GasAExYSBrkr1140001140001031TA6Typ0NoInfoNoInfoYearBuiltNoInfo00NoInfoNoInfoY36560000NoInfoNoInfoNoInfo072006WDAbnorml84500
1454145520FV62.07500PavePaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story7520042005GableCompShgVinylSdVinylSdNone0.0GdTAPConcGdTANoGLQ410Unf08111221GasAExYSBrkr1221001221102021Gd6Typ0NoInfoAttchd2004.0RFn2400TATAY01130000NoInfoNoInfoNoInfo0102009WDNormal185000
1455145660RL62.07917PaveNoInfoRegLvlAllPubInsideGtlGilbertNormNorm1Fam2Story6519992000GableCompShgVinylSdVinylSdNone0.0TATAPConcGdTANoUnf0Unf0953953GasAExYSBrkr95369401647002131TA7Typ1TAAttchd1999.0RFn2460TATAY0400000NoInfoNoInfoNoInfo082007WDNormal175000
1456145720RL85.013175PaveNoInfoRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story6619781988GableCompShgPlywoodPlywoodStone119.0TATACBlockGdTANoALQ790Rec1635891542GasATAYSBrkr2073002073102031TA7Min12TAAttchd1978.0Unf2500TATAY34900000NoInfoMnPrvNoInfo022010WDNormal210000
1457145870RL66.09042PaveNoInfoRegLvlAllPubInsideGtlCrawforNormNorm1Fam2Story7919412006GableCompShgCemntBdCmentBdNone0.0ExGdStoneTAGdNoGLQ275Unf08771152GasAExYSBrkr1188115202340002041Gd9Typ2GdAttchd1941.0RFn1252TATAY0600000NoInfoGdPrvShed250052010WDNormal266500
1458145920RL68.09717PaveNoInfoRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story5619501996HipCompShgMetalSdMetalSdNone0.0TATACBlockTATAMnGLQ49Rec102901078GasAGdYFuseA1078001078101021Gd5Typ0NoInfoAttchd1950.0Unf1240TATAY3660112000NoInfoNoInfoNoInfo042010WDNormal142125
1459146020RL75.09937PaveNoInfoRegLvlAllPubInsideGtlEdwardsNormNorm1Fam1Story5619651965GableCompShgHdBoardHdBoardNone0.0GdTACBlockTATANoBLQ830LwQ2901361256GasAGdYSBrkr1256001256101131TA6Typ0NoInfoAttchd1965.0Fin1276TATAY736680000NoInfoNoInfoNoInfo062008WDNormal147500